Beyond Biology: A Systematic Review of the Effects of Gender on Functional Outcomes

doi:10.21203/rs.3.rs-7972914/v1

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Research Article

Beyond Biology: A Systematic Review of the Effects of Gender on Functional Outcomes

https://doi.org/10.21203/rs.3.rs-7972914/v1

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Background

Biological sex and sociocultural gender may influence changes in health status critical to functional outcomes, yet scientific evidence of their effects on functional capacity remain uncertain. This research synthesized the scientific evidence on the impact of sex and gender attributes on functional outcomes.

Methods

Medline, Embase, PsycINFO, Web of Science and CINAHL were searched for studies using standardised measures to capture the association between sex and gender attributes and functional outcomes. Study quality was assessed using the National Institutes of Health assessment tools. Results were grouped by attribute and functional category. The certainty of evidence assessment considered consistency in the reported associations and study quality.

Results

Of the 12,948 records identified, ten studies (two of excellent, three of good, and five of fair quality) with a total of 10,506 participants (88.2% male) reported on the association between attributes of gender (e.g., gender identity, roles, and adherence to masculine norms) and physical, behavioral, and daily life functioning. None of the studies addressed sex attributes. The substantial heterogeneity in the consistency and significance of the findings across sex-specific and mixed-sex samples resulted in very low overall certainty in the evidence.

Conclusion

There is a gap in the evidence on the effect of sex attributes on functional outcomes and substantial heterogeneity in the reported associations between various gender attributes and functional outcomes. Further, the existing evidence concerns largely male participants. Research directed at separating the effects of sex from those of gender to determine their independent contributions to variability in outcomes across the sexes is timely.

Funding: Global Brain Health Institute, Alzheimer’s Association, and Alzheimer’s Society UK Pilot Award for Global Brain Health Leaders (GBHI ALZ UK-23-971123); Operating Grant: Summer Institute in Sex and Gender+ Science Host / Subvention de fonctionnement: Atelier d’été sur la science du sexe et du genre+ (SGS 517054); Canada Research Chairs Program for Neurological Disorders and Brain Health (CRC-2021-00074).

Androgyny

Biological sex

Femininity

Gender and sex assessment

Health

Masculinity

Sex hormones

Sociocultural gender

Societal expectations

Why this matters: A person’s sex (reflected in their physiological characteristics) and gender (reflected in their social identity and context) can affect their health and their functioning in family and society. These are called functional outcomes.

Why we need to study this: Learning more about this relationship can advance person-centered research and care.

What we did: We looked at studies that explored how sex and gender characteristics affect everyday activities and functional outcomes. We also assessed the quality of the studies. This helped us understand the strength of the evidence generated.

What we found: We found ten studies that looked at how gender characteristics affect functional outcomes and none that looked at sex. The studies differed in quality and the means by which they measured gender and functional outcomes. As a result, the certainty of the evidence was not strong. However, it was evident that gender plays a role in functional outcomes.

What are next steps: Future research should look at sex characteristics along with gender characteristics to understand their separate and cumulative effects on function. This will result in research with greater applicability to the people affected, and a more advanced understanding of how to optimize functional outcomes in different contexts.

• Past reviews have explored the effect of sex and gender on functional outcomes in adults; however, their focus was only on binary sex and/or gender.

• We examined the scientific literature on the effects of sex and gender attributes on functional outcomes.

• We found no scientific evidence on the associations between sex-related attributes (i.e., chromosomes, hormones, and anatomy) and functional outcomes.

• We presented a comprehensive synthesis of identified associations between gender attributes (i.e., social roles, behaviors, expressions, and identities) of 10,506 research participants (88.2% male) and 12 functional outcomes.

• The results of our evidence synthesis have important implications for advancing rehabilitation research and practice, and provide a basis for a broad range of future scientific and clinical inquiries.

The concept of functional capacity concerns purposive behaviors in various contexts and is defined as “an individual's ability to perform tasks and activities, particularly those required for daily living or work, to a specific level of ability” [1]. It refers to how well a person can execute the actions and duties that they need to, want to, or are expected to complete, taking into consideration physical, cognitive, emotional, and social factors. Assessing functional capacity is essential for predicting and understanding functional outcomes, as it provides a measure of what a person is capable of doing, which, in turn, influences what they are likely to achieve in their daily life. Among the factors that reflect on functional capacity are those related to sex and gender, both highly relevant in health research [2] and capable of shaping one’s capacity to function.

Sex is a biological construct, encompassing physical and physiological attributes of humans, including chromosomes, hormones, and anatomy. Gender is a sociocultural construct that encompasses social roles, behaviors, expressions, and identities of people [3]. Studies of sex differences have yielded important insights into the underlying mechanisms influencing health-related capacity to function, citing variations in neural plasticity, inflammatory responses, hormones, and bodily structure as factors that play a role in how people sustain or regain functional capacity [4, 5]. In the context of daily activities and behaviors, the existing research highlighting differences in functional outcomes of people of different genders implicates gender-specific roles, responsibilities, and relationships in family and society [6–8].

To date, several systematic reviews have provided insights into the effects of sex and gender effects on functional outcomes. The evidence included in these systematic reviews synthesized data based on binary classifications of people (e.g., male versus female and/or men versus women, with specific disorders and in general) or the effect of access to and quality of care on the functional outcomes of specific groups of people (e.g., trans-feminine, trans-masculine people, or both) [9–13]. While significant, these general findings limit the fulsome understanding of the effects of sex and gender and their relevance to person-centered care, in light of the vast complexity in biological and social attributes that compose each person’s sex and gender. As a result, there remains a significant gap in knowledge regarding the continuum of sex and gender attributes and their relevance to functional outcomes. To address this gap, the objectives of the present systematic review were three-fold: (1) to identify and critically appraise studies that used standardized measures to capture the effects of sex and/or gender attributes on functional outcomes; (2) to categorize sex and gender attributes, their related measures, and measures of outcomes; and (3) to examine the relevance and certainty of the associations between sex and gender attributes and clinical outcomes.

2.1. Protocol and registration

This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and is part of a larger study investigating sex and gender affects in health and functioning. Prior to initiation of the present work, the protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on September 2, 2023 (CRD42023456917).

2.2. Search strategy

The search strategy (Supplementary File 1) was developed in collaboration with a medical information specialist at a large rehabilitation teaching hospital. Four electronic databases (MEDLINE (Ovid), Embase (Ovid), PsycINFO (Ovid), Web of Science and CINAHL (EBSCOhost)) were searched from inception until November 20, 2023. The search results from each database were imported into Covidence.

2.3. Eligibility criteria based on PICOS framework

The eligibility criteria were set a priori, using the PICOS framework:

Population (P): people older than 16 years old of all sexes (i.e., male, female, other) and genders (i.e., man, woman, non-binary, etc.)
Intervention (I): not applicable; only observational studies were included
Comparisons (C): standardized measures, tools, scales, or normative values used to capture attributes of sex (i.e., sex-related hormones, genetic factors, anatomy, etc.), and/or attributes of gender (i.e., gender traits, such as masculinity, femininity, androgyny, and gender roles, etc.)
Outcomes (O): functional outcomes (i.e., outcomes related to experience in a broader term of health and behavior, impacting people’s ability to perform social roles, responsibilities and other tasks of daily living).
Study design (S): observational studies of any design (i.e., quantitative, mixed methods, cohort, cross-sectional, case control).

Human biology exists within the context of a dynamic and evolving social environment, which, in turn, is shaped by societal expectations based on one’s biological sex. In the studies included in this review, researchers frequently used the terms male/female and men/women interchangeably, without distinguishing between these constructs. In the absence of ability to distinguish between these terms, we opted to use the terms ‘male’ and ‘female’ to maintain consistency in data synthesis and reporting. This decision was arbitrarily set, and it should not be assumed that male = men and female = woman.

2.4. Inclusion and exclusion criteria

Studies were included if they met the following criteria: (i) investigated the association between a sex and/or gender attribute of adults and functional outcomes; (ii) the sex and/or gender measure was standardized and used by at least two different teams of investigators; and (iii) the full text was written in English and published in a peer-reviewed journal. Studies in which sex or gender was captured by self-identification were excluded. Letters to the editor, case reports, dissertations, and studies with no primary data were also excluded.

2.5. Study selection process

Multiple reviewers were involved in the abstract screening stage such that at least two reviewers independently screened titles and abstracts to identify potentially relevant studies. In the full-text review, the two primary authors (AB and AI) independently assessed the full texts to determine if they met the inclusion criteria. Studies that did not meet inclusion criteria were excluded and the reason for exclusion was documented. The senior author (TM) reviewed the quality of the first and second levels of screening. Discrepancies were resolved by group discussion.

2.6. Data extraction

A standardized data extraction template was used by two primary authors (AB and AI). The data extraction template included (i) study information (i.e., authors, publication year, country, location of research, objective, study design, inclusion/exclusion criteria, sample size); (ii) participant characteristics (i.e., age, sex and any other reported parameters relevant to analysis); (iii) sex and/or gender measures used, outcome measures, statistical analyses; and (iv) outcomes and key findings related to sex and gender. Two primary authors (AB and AI) extracted the data independently, and the third (TTS) and senior author (TM) reviewed for data for accuracy. Discrepancies were resolved by group discussion.

2.7. Data synthesis

Heterogeneity across PICOS characteristics precluded meta-analysis in its classic form. Best-evidence synthesis approach [14, 15] was used to organize findings by tabulation and qualitative description, grouping studies into two main categories: sex effects and gender effects, and further dividing them by attribute, and by outcome. All effect sizes relevant to the research question were extracted. All attributes of sex and/or gender, significant and non-significant, as reported by authors, were considered associations and not causal factors.

To capture and interpret expression of sex and/or gender attributes in the results of included studies, variables included in statistical analysis were categorized using PROGRESS framework [16], namely, place of residence, race/ethnicity/culture/language, occupation, religion, education, socioeconomic status, and social capital.

2.8. Quality and risk of bias assessment

Study quality was assessed independently by two primary authors (AB and AI) using the National Institutes of Health study quality assessment tools for controlled intervention studies and pre-post studies with no control group [17]. The corresponding checklists allowed the authors to appraise each study according to the criteria most critical to the external and internal validity of its design. An overall quality rating was assigned based on the presence of potential biases, reported as “Yes,” “Not reported,” “Cannot determine”. The Scottish Intercollegiate Guidelines Network [18] methodology was utilized to summarize the evidence as follows: (i) “++” when all of the quality criteria were fulfilled (allowing one “Cannot determine”); (ii) “+” when most of the criteria were fulfilled; and (iii) “-” when few of the criteria were fulfilled. Studies were classified as high (++), moderate (+) or low (-) quality based on the criteria outlined. Discrepancies were documented and resolved through group discussion.

Studies were not excluded based on the quality assessment, but quality was considered in the interpretation and reporting of results.

2.9. Sensitivity analysis

Sensitivity analyses were conducted to examine the consistency of associations [20]. The plan was to position results by study outcome and measure of sex and/or gender attribute, reporting direction of associations (positive, negative, non-significant association) and study quality to visualize results across studies. Additional analysis was conducted by stratifying results of studies published prior to 2016, and those published in 2016 or later. This approach allowed for exploration of whether sex and gender consideration have received greater attention in research on functional outcomes following the publication of the SAGER guidelines in early 2016 [21].

2.10. Certainty assessment

Certainty of evidence assessment was completed qualitatively, incorporating the study quality assessment based on risk of bias assessment across six domains, following criteria developed in prior research [19]. The certainty of the evidence was rated as high if two or more excellent quality studies coming from different groups of researchers were concordant regarding the observed association between similar sex (i.e., testosterone, estrogen, etc.) and/or gender attribute (i.e., femininity, non-conformity, etc.) and functional outcome and no discordant results from studies of equal quality were present. The certainty of evidence was assigned as moderate if two or more studies of good and/or excellent quality were concordant in their results, with a maximum of one discordant result from studies of good or excellent quality. The low certainty rating was assigned if at least two fair and/or good quality studies were concordant in results, with a maximum of one discordant result from studies of fair or good quality. In all other situations, the certainty was rated as very low.

2.11. Missing data

The approach to any missing or unclear data was to contact the corresponding author of included studies for clarification. In the case of duplicate publications and companion papers of a primary study, the approach was to maximize data use through inclusion of all available data with the original study being given priority for inclusion.

3.1. Search results

Searches identified 19,538 records, with 12,948 studies remaining after duplicate removal (Supplementary File 1). After screening, 175 met the criteria for full-text review, of which ten studies met the inclusion criteria for data extraction and synthesis [22–31]. Reasons for the exclusion of the remaining 163 studies were documented (Supplementary File 2). The selection process is presented in the PRISMA flow diagram (Fig. 1).

3.2. Study characteristics

The key findings from the ten reviewed studies, including study design, sample size, demographic characteristics, measures of sex, gender, and outcome, and study results are presented in Tables 1 and 2 (Table 1, 2). These studies were comprised of 13 cohorts: male only [25, 30], female only [31], combined males and females [23, 24, 27–29] and males and females separately [22, 26].

Table 1
Summary of all included studies investigating effect of gender attributes on functional outcomes
Author (year); Journal; Country; Region; City; Location of research; Study Quality: Fair (“-”), Good (“+”), Excellent (“++”)	(1) Objective (2) Design (3) Follow up/assessment times, if any (4) Inclusion criteria a. Social b. Clinical c. Behavioural d. Other (5) Exclusion criteria a. Social b. Clinical c. Behavioural d. Other	(1) Total sample size, n (M/F) (2) Attrition, % (if multiple assessments) (3) Age (mean ± SD) or range (4) Sex, %M (5) Other parameters reported (6) Parameters considered in analysis a. Primary predictor(s)* b. Other	(1) Measure of sex and/or gender (2) Measure of outcome(s) (3) Statistical analysis/analysis controls for	(1) Sex- and/or gender- related results (2) Other parameters related to outcome(s) (3) Researcher notes
1. Helgeson VS. (1991); Psychosom Med; USA; Colorado/New York; Denver/Long Island; Hospital; Fair	(1) Investigate relationship b/w masc & social support w/ recovery from MI (2) Longitudinal (3) Follow up at 3mos, 6mos, 12mos (4) a. age ≤ 70 b. Dx of acute MI c. NR (5) a. NR b. NR c. NR d. NR	(1) 90 (70M/20F) (2) 12mos = 3% (3) 37–70 (4) 78% M (5) Education, religion, occupation, SES (6) a. Masc b. Age, SES	(1) PAQ (2) Self-report: rehospitalization (3) Stepwise logistic regression analysis, stepwise multiple regression analysis / sex, Peel index, psychological distress, CHD risk factors	(1) Masc did not sig predict rehospitalization (2) No sig assoc b/w sex/age/SES & recovery (3) Spouse disclosure was most sig indep predictor of rehospitalization
2. Kerr P, et al. (2021); J Psychosom Res; Canada; Quebec; Montreal; Community; Good	(1) Measure effect of GR on MH & workplace stress in psychiatric hospital workers (2) Exploratory retrospective (3) NA (4) a. NR b. NR c. Employed at psychiatric hospital d. NR (5) a. NR b. NR c. NR d. NR	(1) 192 (55M/137F) (2) NA (3) (40.5 ± 12.14), 18–72 (4) 29% M (5) Occupation, education, social capital (6) a. Masc/fem b. Occupation, age	(1) BSRI-SF (2) MBI (3) Structural equation model (path analysis) / other job strain factors	(1) Masc & fem had neg assoc w/ burnout Sx (2) Age assoc w/ ↓ burnout Sx & social support; pos assoc b/w occupation & psychological demands (3) GR endorsement assoc w/ psychosocial outcomes
3. Kuntsche A, et al. (2019); Drug Alcohol Depend; Switzerland; Community; Good	(1) Examine relationship b/w GRA, WFC & alcohol consumption (2) Cross-sectional (3) NA (4) a. NR b. NR c. Parents of 3–6 y/o children; employed d. NR (5) a. NR b. NR c. NR d. NR	(1) 305 (142M/163F) (2) NA (3) M (40.5 ± 4.6) F (37.1 ± 4.5) (4) 47% M (5) Occupation (6) a. trad/non-trad GRA; binary sex b. WFC	(1) ATWS (2) Alcohol frequency & quantity (3) Regression analyses / occupation, age	(1) W/o WFC, M & F parents w/ trad GRA had ↓ alcohol use than non-trad parents. W/ ↑ WFC, M & F parents w/ trad GRA had ↑ alcohol consumption compared to non-trad parents. (2) W/ ↑ WFC, M had ↑ annual freq & F had ↑ daily quantity of alcohol use (3) GRA have a moderating effect on relationship b/w WFC & alcohol use
4. Lu YM, et al. (2022); J Back Musculoskelet;Taiwan; Kaohsiung; Hospital; Good	(1) To investigate sex & gender effects on disability, HRQOL in patients w/ low back pain (2) Cross-sectional (3) NA (4) a. Age ≥ 18yrs b. Low back pain w/ or w/o leg pain; Dx of low back pain & non-specific back pain c. Absence of physical limitations affecting ability to complete the questionnaire d. Able to read traditional Chinese (5) a. NR b. Other types of pain (knee OA, soft tissue trauma of lower leg & general absence of low back pain) c. NR d. NR	(1) 93 (42M/51F) (2) NA (3) 21–87 (59.1 ± 15.9) M 21–84 (56.8 ± 19.5) F 28–87 (61 ± 12) (4) 44% M (5) Age, marital status, education, occupation (6) a. Masc/fem; binary sex b. Age, marital status, education, occupation	(1) BSRI (2) ODI; HRQOL-SF-36; VAS (3) χ² test / binary sex, GRO, marital status, education, occupation; ANOVA/ binary sex, GRO; post-hoc/GRO	(1) F had ↑ HRQOL w/ ↓ impact scores on VT & MH subscales; masc characteristics had ↓ impact scores in RP, SF, REL & MH, F had sig ↑ disability compared to M (2) NR (3) Neg impact score indicates a poorer quality of life; good masc characteristics may ↑ HRQOL in patients w/ back pain
5. McHale S, et al. (1984); Monogr Soc Res Child Dev; USA; Pennsylvania; Community; Fair	(1) Examine how parents’ sex-role orientation & employment assoc w/involvement in child-oriented activities (2) Longitudinal (3) F/u at 1 year (4) a. Married, parents b. NR c. NR d. NR (5) a. NR b. NR c. NR d. NR	(1) 68 (34 M/34 F) (2) NR (3) M (22.9 ± 3.0) F (20.4 ± 1.9) (4) 50% M (5) Age, education, race, (6) a. trad/non-trad GRA, masc/fem, binary sex b. NR	(1) PAQ, ATWS (2) Self-reported involvement in home/childcare (3) Repeated-measures ANOVA; correlations / NR	(1) M w/ non-trad GRA have ↑ involvement in home/childcare; Fem traits in M predict likelihood of involvement in home/childcare; NS assoc b/w masc/fem in F & home/childcare (2) NR (3) No sig correlations b/w parents’ GI & behaviors
6. McLaughlin K, et al. (2010); Nurs Educ Today; United Kingdom; Ireland; School; Fair	(1) Examine how gender & views of nursing in nursing students relate to course completion (2) Longitudinal (3) Follow up at end of course (4) a. UK nursing students b. NR c. NR d. NR (6) a. NR b. NR c. NR d. NR	(1) 384 (34M/350F) (2) 12% (3) 20.7 ± 3.95 (4) 86% (5) Age, education (6) a. Masc/fem b. NR	(1) BSRI (2) Self-reported nursing course completion (3) χ² analyses / NR, multivariate ANOVA/NR, logistic regression/ GRO	(1) Masc/fem was not predictive of nursing school completion (2) NR (3) Students most likely to withdraw viewed nursing as appropriate for M & F
7. Milner A, et al. (2018); 27.25, 27.48 Am J Mens Health; Australia; Victoria; Melbourne; Community; Excellent	(1) To examine whether poorer MH among M in M-dominated occupations is related to harmful gender norms (2) Cross-sectional (3) NA (4) a. Men aged 18-55yrs b. NR c. NR d. Data obtained from Australian Longitudinal Study on Male Health (Ten to Men) (5) a. NR b. NR c. NR d. NR	(1) 8788 (8788M/0F) (2) NA (3) 18–55 (4) Age, education, relationship status, income level (5) 100% M (6) a. Masc b. Age, education, relationship status, income level	(1) CMNI-22 (2) SF-12 – MH Subscale (3) Multiple linear regression models / occupational gender ratio, age, education, relationship status, income level	(1) Many CMNI-22 subscales were assoc w/ ↓ MH for M (2) After controlling covariates, ↑ self-reliance was assoc w/ ↓ MH (3) M in M-dominated fields tend to adhere to specific gender norms
8. Po Yee Lo I, et al. (2019); Arch Sex Behav; United Kingdom; Oxford; USA; Louisiana; Texas; Arlington; Victoria; Abbotsford; Community; Excellent	(1) Examine effects of different types of GR on MH (2) Cross-sectional (3) NA (4) a. Female, aged 18–35yrs b. NR c. Speak & read Chinese, identifies as lesbian d. Citizen of Hong Kong (5) a. NR b. NR c. NR d. NR	(1) 438 (0M/438F) (2) NA (3) 18–35 (24.67 ± 4.6) (4) 0% M (5) Occupation, education, relationship status, religion (6) a. Masc/fem b. NR	(1) BSRI, (2) RSES (3) ANOVA, structural equation model /age	(1) Strong masc & adg traits sig assoc w/ ↑ self-est; strong fem traits were sig assoc w/ ↓ self-est (2) NR (3) ↑ masc & fem traits can promote psychological health
9. Schopp C, et al. (2011); Brain Injury; Australia; Victoria; Abbotsford; Hospital; Fair	(1) Examine link b/w masc role & psychosocial, rehab outcomes in M w/ TBI (2) Longitudinal (3) Follow up at 1 year, 2 year, 5 year (4) a. NR b. Primary Dx of TBI c. Inpt acute care rehab ≥ 1 yr d. NR (5) a. NR b. NR c. NR d. NR	(1) 33 (33M/0F) (2) NR (3) 18–91 (41.1 ± 19.2) (4) 100% M (5) Education (6) a. Masc norms b. NR	(1) CMNI, GRCS (2) FIM, SFS (3) Spearman correlations, Wilcoxon rank sum tests / NR	(1) Masc norms (Winning, Pursuit of Status) had pos assoc w/ functional indep; life satisfaction linked w/ Power Over Women, Playboy traits; No sig effects b/w GRCS, life satisfaction, functional gains (2) NR (3) Specific masc traits assoc w/ functional, psychological outcomes for M w/ TBI
10. Zeldow PB, et al. (1987); J Pers Assess; USA; Illinois; Chicago; Community; Fair	(1) Examine relationship b/w masc & fem with adjustment, interpersonal functioning in medical school (2) Longitudinal (3) Follow up at 21mos (4) a. NR b. NR c. NR d. 1st year medical students (5) a. NR b. NR c. NR d. NR	(1) 115 (67M/32F) (2) 21mos = 18% (3) 25.4 (4) 58% M (5) NR (6) a. Masc/fem b. NR	(1) PAQ (2) RSES, impaired functioning (drug, alcohol use) (3) Correlation test, Maximum likelihood logit regression analysis / NR	(1) ↑ masc assoc w/ ↑ self-est; ↑ fem assoc w/ ↓ alcohol use , (2) NR (3) Fem traits predict drug use but not clinically sig

Note: In this table we have used the terms ‘sex’, ‘male’ & ‘female’ when researchers reported results based on biological attributes of their participants, regardless of the term used in the original text. The use of the term ‘primary predictor’ does not imply causality but identifies the predictor as a main factor possibly associated with an outcome.

Abbreviations: Adg, androgynous; ANOVA, analysis of variance; ATWS, Attitude Toward Women Scale; BSRI-SF, Bem Sex Role Inventory-Short Form; BSRI, Bem Sex Role Inventory; CHD, coronary heart disease; CMNI, Conformity to Masculine Norms Inventory; Dx, diagnosis; F, females; F/u, follow-up; Fem, femininity; FIM, Functional Independence Measure; GR, gender role; GRA, gender-role attitudes; GRCS, Gender Role Conflict Scale; GRO, gender role orientation; HRQOL-SF, health-related quality of life short form; Indep, independent; Inpt, inpatient; M, males; Masc, masculinity; MBI, Maslach Burnout Inventory; MH, mental health; MI, myocardial infarction; Mos, months; NA, not applicable; Neg, Negative; NR, not reported; NS, non-significant; OA, osteoarthritis; ODI, Oswestry Disability Index; PAQ, Personal Attributes Questionnaire; Pos, Positive; Rehab, rehabilitation; REL, role-emotional; RP, role-physical; RSES, Rosenburg Self-Esteem Scale; Self-est, self-esteem; SES, socioeconomic status; SF, social functioning; SFS, Satisfaction with Life Scale; Sig, significant; Sx, symptoms; Trad, traditional; VAS, visual analog scale; VT, vitality; W/o, without; WFC, work-family conflict; Y/o, year-old; Yrs, years; ↑, increased; ↓, decreased

Table 2
Results of analyses of included studies investigating effect of gender attributes on functional outcomes
Author (year); Journal; Country; Region; City; Location of research; Study Quality: Fair, Good, Excellent	(1) Gender scores (2) Outcome scores (3) Results of statistical analysis
11. Helgeson VS. (1991); Psychosom Med; USA; Colorado/New York; Denver/Long Island; Hospital; Fair	(1) Descriptive data NR (2) Descriptive data NR (3) Regression analysis: rehospitalization (sex NS; masc NS; Peel index β = .11, p = .01; spouse disclosure β=-1.09, p = .01)
12. Kerr P, et al. (2021); J Psychosom Res; Canada; Quebec; Montreal; Community; Good	(1) BSRI fem/masc mean (SE): 5.98 (0.04)/4.56 (0.05); F, 6.02 (0.05)/4.52 (0.06); M, 5.85 (0.07)/4.67 (0.09) (2) MBI mean (SE): 25.47 (0.83); F, 25.88 (0.94); M, 24.39 (1.71) (3) Structural equation model: burnout, masc (β=-.17, z=-2.67, p = .008), fem (β=-.14, z=-2.17, p = .03)
13. Kuntsche A, et al. (2019); Drug Alcohol Depend; Switzerland; Community; Good	(1) ATWS, %trad GRA: M 42.3%, F 52.8% (2) Alcohol freq, mean ± SD: M 148.3 ± 114.7 days, F 79.1 ± 85.8 days; alcohol quantity, mean ± SD: M 31.9 ± 20.6g, F 20.3 ± 13.7g (3) Regression analysis: alcohol freq, GRA (M, β = .366, p < .05; F, NS, p > .05); WFCGRA (M, β=-.370, p <* .05; F, NS, p > .05); alcohol quantity, GRA (M, NS, p > .05; F, β.376, p < .01); WFC*GRA (M, NS, p > .05; F, β=-.336, p < .05)
14. Lu YM, et al. (2022); J Back Musculoskelet;Taiwan; Kaohsiung; Hospital; Good	(1) Distrib (n,%) for M: masc = 12 (28.6%), fem = 4 (9.5%), adg = 12 (28.6%), UD = 14 (33.3%), F: masc = 8 (15.7%), fem = 14 (27.5%), adg = 17 (33.3%), UD = 12 (23.5%) (2) Mean ± SD VAS Score for back: M = 3.0 ± 2.5, F = 3.5 ± 2.5; Mean ± SD VAS score for leg: M = 2.8 ± 2.7 F = 2.6 ± 2.4; Mean ± SD ODI score: M = 29.2% ±20.7, F = 37.3% ±19.7, Masc = 30.7% ±17.8, Fem = 37.8% ±22.2, Adg = 30.1% ±21.5, UD = 37.1% ±20.2; Domains of SF-36, mean ± SD: PF = 50.9 ± 25.9, RP = 37.4 ± 45.1, BLP = 54.2 ± 24.0, GH = 52.6 ± 18.7, VT = 56.1 ± 18.6, SF = 66.1 ± 22.2, RE = 50.7 ± 47.0, MH = 63.0 ± 17.7 (3) ANOVA: sig group differences in GRO in RP (p = 0.03), VT (p < 0.001), SF (p = 0.045, REL (p = 0.007) & MH (0.01); Post-hoc analysis: sig difference (p < 0.05) b/w masc & UD for RP, VT, SF, REL & MH, sig difference (p < 0.05) b/w adg & UD for VT, REL, MH
15. McHale S, et al. (1984); Monogr Soc Res Child Dev; USA; Pennsylvania; Community; Fair	(1) PAQ mean ± SD: F/mother, fem = 25.5 ± 3.7, masc = 18.8 ± 4.1; M/father, fem = 23.1 ± 3.4, masc = 23.2 ± 4 ATWS mean ± SD: F = 28.8 ± 8.7; M = 26.6 ± 6 (2) Descriptive data NR (3) Correlations of Characteristics w Level of Child-Involvement (Total score): F/mother, masc, r= -0.24, NS fem, r = 0.19, NS; M/father, masc, r= -0.11, NS, fem, r = 0.23, NS
16. McLaughlin K, et al. (2010); Nurs Educ Today; United Kingdom; Ireland; School; Fair	(1) Descriptive data NR (2) Total course completion (N, %) = 307, 88% (3) Logistic regression: sig. predictor of course completion was gendered view of nursing, participants who believed the gender-neutral careers were more appropriate for both M & F were most likely to withdraw (b = .166, p < 0.01).
17. Milner A, et al. (2018); 27.25, 27.48 Am J Mens Health; Australia; Victoria; Melbourne; Community; Excellent	(1) Total sample, Mean score: 27.32, CI_95% [27.25, 27.48] (2) Total sample, Mean score: 50,64, CI_95% [50.46,50.81) (3) Multiple linear regression models’ effects of CMNI-22 on SF-12 – MH Subscale [β, CI_95%, p value], Total scale= [-0.17, -0.20, -0.13, < .001], Emotional control= [− 0.42,−0.57, − 0.29,< .001], Pursuit of status=[0.04, − 0.23, 0.14, .65], Playboy= [− 0.75,−0.90, − 0.61] < .001], Heterosexual presentation=[0.04, 0.07, 0.16, .450], Dominance=[− 0.20, − 0.38, 0.03, .020], Power over women = − 0.41, − 0.60, − 0.24, < .001], Risk taking = − 0.02,−0.18, 0.14, .830], Winning=[− 0.36, − 0.52, − 0.18, .010], Reliance= [− 1.50, − 1.66, − 1.33, < .001], Violence = − 0.48,−0.60, − 0.36],< .001], Work={0.37,0.22, 0.53, < .001}
18. Po Yee Lo I, et al. (2019); Arch Sex Behav; United Kingdom; Oxford; USA; Louisiana; Texas; Arlington; Victoria; Abbotsford; Community; Excellent	(1) Descriptive data NR (2) Descriptive data NR (3) ANOVA, mean scores: masc, self-est = 3 ± 0.47; fem, self-est = 2.70 ± 0.49; Adg, self-est = 3.03 ± 0.47; UD, self-est = 2.67 ± 0.49; Path model, direct effect: (V₁→V₂)=(β, SE, t, p-value), Masc → self-est = (.17, .06, 2.74, .006); Fem→ self-est = (− .17, .06, − 2.65, .008); Adg → self-est = (.22, .06 3.67, < .001)
19. Schopp C, et al. (2011); Brain Injury; Australia; Victoria; Abbotsford; Hospital; Fair	(1) CMNI subscales mean ± SD: winning, emotional control, risk-taking, violence, power over women, dominance, playboy, self-reliance, primacy of work, pursuit of status = 14.6(± 3.7), 15.1(± 5.2), 13.9(± 4.5), 9.9(± 4.8). 8.7(± 3.7), 4.6(± 2), 13.7(± 6.4), 6.3(± 3), 10.8(± 3.4), 10(± 2.2) GRCS subscales mean ± SD: PSC, RE, CWF = 45.5(± 11.3), 33.7(± 11.9), 18.3(± 6.3) (2) Mean ± SD for all values, FIM at d/c : 103.1(± 17.9), FIM from adm to d/c: 57.2(± 23.1), FIM from adm to f/u: 47.4(± 22.6); SFS: 19.6(± 8.8) (3) Correlations b/w CMNI & SLS, FIM from adm to d/c, FIM from adm to f/u, p < 0.05, p < 0.01: CMNI total=-0.12, -0.15, 0.11; winning= -0.17, 0.38, 0.39; risk-taking=-0.18, 0.2, 0.16; violence = 0.01, 0.02, -0.03; power over women= -0.53, -0.35, -0.04; dominance=-0.01, .01, .13; playboy= -0.44, -0.2, -0.1; self-reliance=-0.37, -0.29, -0.03; primacy of work = .12, -0.12, -0.02; pursuit of status = .02, .41*, .4
20. Zeldow PB, et al. (1987); J Pers Assess; USA; Illinois; Chicago; Community; Fair	(1) PAQ mean ± SD: masc = 21.86 (± 4.42), fem = 23.65(± 3.75) (2) RSES mean ± SD: 6.45(± 0.82); 3.86(± 0.46); alcohol use, mean ± SD: 2.40(± 3.35), drug use, (distri, n, % ) = 23, 20% (3) Correlations: mascself-est (0.38, p < 0.01), masc alcohol use (-0.1, NS); femself-est (.08, NS), femalcohol use (-.24, p < 0.05) Max likelihood logit regression (χ², p value, alcohol use/drug use): masc (.66, NS/.01, NS); fem (1.52, NS/5.2, .023)

Abbreviations: Adg, androgynous; Adm, admission; ANOVA, analysis of variance; ATWS, Attitude Toward Women Scale; BLP, bodily pain; BSRI, Bem Sex Role Inventory; CI, confidence interval; CMNI, Conformity to Masculine Norms Inventory; CWF, conflict between work & family (GRCS subscale); D/c, discharge; F, females; F/u, follow-up; Fem, femininity; FIM, Functional Independence Measure; GRA, gender-role attitudes; GRCS, Gender Role Conflict Scale; GRO, gender role orientation; M, males; Masc, masculinity; MBI, Maslach Burnout Inventory; MH, mental health; NS, non-significant; ODI, Oswestry Disability Index; PAQ, Personal Attributes Questionnaire; PF, physical functioning; PSC, power, success and competition (GRCS subscale); RE, restricted emotionality (GRCS subscale); REL, role-emotional; RP, role-physical; RSES, Rosenburg Self-Esteem Scale; Self-est, self-esteem; SF, social functioning; SFS, Satisfaction with Life Scale; Sig, significant; Trad, traditional; UD, undifferentiated; VAS, visual analog scale; VT, vitality; WFC, work-family conflict

The ten studies included a total of 10,506 participants, 88.2% of which were males. Sample sizes of included studies ranged from 33 participants [25] to 8,788 [30]. The percentage of male participants ranged from 0% [31] to 100% [25, 30]. The age of participants across samples ranged from 18 [25, 29–31] to 91[25] years of age. Two studies did not report on mean age of their participants [27, 30]. One of these studies reported median age (i.e., 59.5 years) and range (37 to 70 years of age) [27]. Another study reported age range only (i.e., 18 to 55 years of age) [30].

3.3. Attributes of sex assessments

None of the included studies assessed associations between attributes of sex and functional outcomes.

3.4. Attributes of gender assessments

All ten included studies assessed associations between attributes of gender and functional outcomes [22–31]. As described in the methods section, inclusion criteria required measures to be used by at least two different teams of investigators for inclusion in this systematic review. Across all ten studies, the attributes of gender were measured using four tools: the Bem Sex Role Inventory (BSRI) [32], used in three studies [24, 28, 31] or its short version (BSRI-SF) [33], used in one study [29], the Conformity to Masculine Norms Inventory (CMNI) [34] or CMNI-22 [35], used in one study each [25][30], the Personal Attributes Questionnaire (PAQ), used in three studies [23, 26, 27], and the Attitude Towards Women Scale (ATWS) [36], used in two studies [22, 26].

Masculinity, femininity, and androgyny traits

Four studies [24, 28, 29, 31] applied BSRI or BSRI-SF to assess masculinity and femininity in male and/or female participants, of which one [31] also studied androgyny in female participants. Three studies used PAQ [23, 26, 27] to study masculinity and femininity in their male and female participants.

Traditional and non-traditional gender role attitudes and norms

Two groups of researchers [22, 26] used the ATWS to measure traditional and non-traditional gender role attitudes in male and female participants separately. One group of researchers used it to study traditional gender role attitudes in the presence or absence of work-family conflict [22], and one study [26] used it to assess non- traditional gender role attitudes. Two groups of researchers studied masculine norms using the CMNI [25, 30]. Milner and colleagues (2018) used the modified 22-item version of the original CMNI version [30].

3.5. Outcome assessment

Ten included studies examined 12 outcomes, including (1) functional independence [25], (2) disability [24], (3) involvement in home and child care [26], (4) nursing program completion [28], (5) life satisfaction [25], (6) quality of life [24], (7) drug involvement and frequency [23], (8) alcohol consumption [22, 23], (9) burnout [29], (10) mental health [30], (11) rehospitalization [27], and (12) self-esteem [23, 31]. We categorised these 12 outcomes into four categories by construct focused on (1) ability to perform roles in daily life, family, and educational contexts, titled functional status and community integration (i.e., functional independence [24, 25]; disability [26]; involvement in home and child care [28]; and nursing program completion [28]); (2) emotional and cognitive experiences of well-being, titled psychological well-being and perceived self-worth (i.e., life satisfaction [25], quality of life [24], self-esteem [23, 31], mental health [30], and burnout [29]); (3) coping mechanisms and behaviors that adversely affect functional outcomes, titled maladaptive behaviors (i.e., drug involvement [23] and alcohol consumption [22, 23]; and (4) health service utilization, termed health service utilization (i.e., rehospitalization [27]).

Functional status and community integration

To measure outcomes falling under functional status and community integration, authors of included studies used the Functional Independence Measure (FIM) [25], the Oswestry Disability Index [37] (ODI) [24], and self-report [26, 28].

Psychological well-being and perceived self-worth

Psychological well-being and perceived self-worth were measured using Rosenberg Self-Esteem Scale [38] (RSES) [23, 31], the 36-Item Short Form Health Survey [39] (SF-36) [24] and its short-form SF-12 [39] [30] Maslach Burnout Inventory [40] (MBI) [29], and the Satisfaction with Life Scale (SWLS) [25].

Maladaptive behaviors

Alcohol consumption was measured using self-report by Kuntsche and colleague [22] and the Alcohol Consumption Index by Zeldow et al. [23], used to quantify alcohol intake from self-reported drinking patterns. Drug involvement and frequency was measured using self-report [23].

Health service utilization

Rehospitalization was captured through self-report via phone interviews and review of medical records in the study of Helgeson et al [27].

3.6. Relationship between gender attributes and outcomes

Gender attributes’ scores, outcome scores and results of statistical analysis on the relationship between gender attributes and outcomes is presented in Table 2 (Table 2). Results are presented visually in Fig. 2.

Functional status and community integration

Four studies [24–26, 28] reported the association between gender attributes and functional status and community integration. Masculine gender role conflict was not significantly associated with functional independence in male persons, and masculine norms (i.e., winning, pursuit of status) were positively associated with functional independence [25]. Non-traditional gender role attitudes were positively associated with greater involvement in home and childcare in males and negatively in females; the same study found no significant association between masculinity or femininity and involvement in home and childcare for either females or males [26]. One study found no significant association between masculinity and femininity and nursing program completion in a sample of male and female persons [28]. Another study did not find significant associations between masculinity, femininity, or androgyny and disability for either females or males [24].

Psychological well-being and perceived self-worth

Masculine norms and role conflict were studied as indicators of life satisfaction [25] and mental health [30] among male participants. There was no significant association between masculine norms and role conflict with life satisfaction [25]; however, Milner et al. found that increased adherence to most masculine norms (i.e., emotional control, playboy, power over women, reliance, violence) was negatively associated with poor mental health outcomes [30].

Lu et al. compared quality of life in males and females of masculine, feminine, androgynous, and undifferentiated gender types, and observed statistically significant differences between certain gender types for five out of eight quality of life domains measured with SF-36: (1) role limitations due to physical health problems, (2) vitality, (3) social functioning, (4) role limitations due to emotional problems, and (5) mental health [24]. Undifferentiated gender type had the largest negative impact on all five domains [24]. Lo et al. (1983) [31] and Zeldow et al. (1987) [23] reported positive associations between masculinity and self-esteem in female and mixed samples, respectively. Lo et al. found that femininity was negatively associated with self-esteem, and androgyny was positively associated with the outcome in a sample of female participants, in reference to females with undifferentiated gender type [31]. Kerr et al. (2022) reported that both high masculinity and femininity were negatively associated with burnout in a mixed sample [29].

Maladaptive behaviors

Zeldow et al. (1987) reported that femininity was negatively associated with outcomes of alcohol consumption and drug involvement [23]. The authors reported that low femininity predicts drug use but not with enough sensitivity to establish clinical significance [23]. The authors also identified non-significant associations between masculinity and the aforementioned outcomes [23]. In a mixed sample, traditional gender role attitudes were found to be positively associated with alcohol consumption in the absence of work-family conflict but negatively associated with the same outcome in the presence of conflict [22].

Health service utilization

Helgeson et al. (2022) did not report statistically significant associations between masculinity and rehospitalization [27].

3.7. Sensitivity analysis

Sensitivity analysis based on gender attribute

The results of the sensitivity analysis across studies examining similar gender attributes suggest that masculinity was not significantly associated with alcohol consumption and frequency of drug use [23], involvement in home and child care [28], nursing program completion [27], and rehospitalization [27]. One study reported that masculinity was negatively associated with burnout in a sample of males and females combined [29], and two studies reported it was positively associated with self-esteem in a sample of lesbians [31]. Study quality varies between fair [23, 26–28], good [29] and excellent [31].

Studies that examined femininity reported that it was negatively associated with burnout [29], alcohol consumption [23], and drug involvement and frequency [23]. No association was found between femininity and involvement in home and child care [26] or nursing program completion [28]. Study quality varied between fair [23, 26, 28], good [29], and excellent [31].

Adherence to masculine norms, examined in two studies [25, 30] with samples of male participants, was positively associated with functional independence [25] and negatively associated with poor mental health [25]. Masculine role conflict, examined in one study [25], was not significantly associated with functional independence or life satisfaction in males. Adherence to traditional gender roles, examined in one study involving male participants, was found to be positively associated with alcohol consumption in the absence of work-family conflict, but negatively associated with the same outcome in the presence of conflict [22]. In one study, [26] non-traditional gender role attitude was positively associated with involvement in home and child care in male participants, and negatively associated with same in females. Study quality spanned fair [25, 26], good [22], and excellent [30].

Sensitivity analysis based on historical evolution of consideration of gender

The SAGER guidelines were introduced in 2016 [21], and therefore results for sensitivity analysis were stratified by studied published before and after 2016 to investigate the effect of these guidelines in study methodology. Results are presented visually in Figs. 3a and 3b.

Five studies were published before 2016 [23, 25–28], of which three used PAQ [23, 26, 27], and one used BSRI [28], ATWS [26], and CMNI [25], each. These studies assessed gender attributes to quantify the association between (1) masculinity, femininity and/or androgyny with nursing program completion [28], rehospitalization [27], involvement in home and child care [26], alcohol consumption and drug involvement [23]; (2) non-traditional gender role attitudes with involvement in home and childcare [26], and (3) masculine norms and masculine role conflict with functional independence and life satisfaction [25]. These studies were all fair in quality. Of these studies, only the study on the association between masculinity with rehospitalization controlled their results for binary sex, peel index, psychological distress, and CHD risk factors [27].

Five studies were published from 2016 onwards [22, 24, 29–31]. Two studies used the BSRI [24, 31] or its short form [29] to describe masculine, feminine, undifferentiated, and/or androgynous gender types in relation to disability [24], quality of life [24], burnout [29], and self-esteem [31]. Lu et al. reported no significant difference in disability outcome between gender identity groups [24]. The same study reported that undifferentiated gender type had the greatest negative impact on five domains of quality of life [24]. In female participants, femininity was negatively associated with self-esteem and androgyny was positively associated with the outcome, in reference to females with undifferentiated gender type [31]. Adherence to masculine norms such as emotional control, playboy, power over women, reliance, violence was negatively associated with poor mental health outcomes [30] and adherence to traditional gender roles was positively associated with alcohol consumption in the absence of work-family conflict, but negatively associated in the presence of conflict [22]. Femininity and masculinity in a sample of males and females was negatively associated with burnout [29].

The quality of these studies varied from good [22, 24, 29] to excellent [30, 31]These studies controlled for different covariates, including binary sex [24], gender role orientation [24], marital/relationship status [24, 30], education[24, 30], occupation [22, 24], age [22, 30, 31], job strain factors [29], occupational gender ratio [30], and income level [30].

3.8. Risk of bias and certainty of evidence

Two studies were rated as excellent [30, 31], three studies as good [22, 24, 29], and five as fair quality [23, 25–28] (Supplementary File 3). All sources of disagreement in the appraisal process and final consensus were documented (Supplementary File 4).

A summary of the certainty (or confidence) in the body of evidence regarding the effect of gender attributes on functional outcomes, by category and specific outcome, based on quality assessment and the number of covariates considered in the analyses, and stratified by male, female, or combined study samples, is presented in Fig. 2. This figure shows a mixture of positive, negative, and non-significant associations between a specific gender attribute and functional outcomes in male, female, and mixed samples. No studies examined the same gender attribute and outcome within the same sex group, limiting comparability across findings. Based on the pre-specified criteria outlined in the methods section, the certainty of evidence for all included studies was rated as very low.

3.9. Missing data

No missing or unclear data were found, and thus it was not necessary to contact the primary authors of any of the included studies.

This systematic review aimed to synthesize evidence on the association between sex and gender attributes and functional outcomes, with the goal of identifying key biological and sociocultural attributes relevant to functional rehabilitation. There were no studies conducted by at least two independent research groups that examined the association between sex-related biological attributes within the same construct and functional outcomes. Ten studies investigated the association between gender attributes and 12 distinct functional outcomes in 10,506 male and female adults, either separately or combined. Given the inherently social orientation of function-focused research, it is not surprising that scientific inquiry emphasizing gender has predominated over that focusing on sex, particularly following the release of the SAGER guidelines in 2016. Our stratification of included studies revealed that, over the span of 32 years (1984–2016), only five of the included studies were published [23, 25–28], whereas five additional studies were published in the short span of eight years (2016–2023) [22, 24, 29–31]. It is critical for the literature to expand to a concurrent emphasis on sex, through assessment of biological attributes, and their interaction with sociocultural gender, to further investigate how sex and gender shape functional outcomes. This integrated perspective and the implementation of assessment measures that capture sex and gender attributes through non-binary approaches is essential for capturing diversity in identities and for advancing person-centered research and practice. Although limited in scope and of very low certainty, the results of this systematic review do not support male and female differences in functional outcomes based on gender attributes, but rather support the hypotheses that gender is part of a complex and dynamic social network, with an effect on functional status and community integration, psychological well-being and perceived self-worth, behaviors, and health service utilization.

Gender attributes and categories of outcomes

The results of three studies [25, 26, 28] that examined associations between gender attributes and functional status or community integration showed mostly non-significant findings. The significant associations reported between non-traditional gender roles in males and females and caregiving (positive in males and negative in females)[26] align with prior research highlighting gender-based division of labour in traditional family structures and society [41].

One study included in this systematic review examined associations between masculine gender traits and gender role conflict as predictors of life satisfaction, reporting that masculine norms and gender role conflict were not significantly associated with life satisfaction in male participants [25]. Similarly, a recent systematic review of binary gender differences in life satisfaction of 1,801,417 participants across 166 countries, reported that the direction of gender differences in life satisfaction was inconsistent across age and regional groups [42]. Two groups of researchers reported positive associations between masculinity and self-esteem in a sample of females only and mixed samples [23, 31]. Additionally, Lo et al. (1983) found a negative association between femininity and self-esteem, and a positive association between androgyny and self-esteem in the female sample [31]. Kerr et al. (2022) reported that high levels of both masculinity and femininity were negatively associated with burnout [29]. Milner et al. (2020) demonstrated that adherence to traditional masculine norms was negatively associated with performance on the mental health subscale of the SF-12 (higher scores indicating better mental health) [30]. Taken together, these results suggest that gender effects on psychological well-being and perceived self-worth are likely shaped by the degree of adherence to gender norm expectations. However, Kerr et al.’s finding that high masculinity and high femininity, in a mixed-sex sample, were negatively associated with burnout supports the idea that alignment with and performance of traditional gender roles may come at a cost. The association with burnout specifically raises concern about the pressures of adopting normative behaviours traditionally aligned with the respective genders [29]. In contrast, Lo et al.’s finding that androgyny was positively linked to self-esteem in females (in reference to females with undifferentiated gender type) aligns with the Gender Schema Theory [43], which holds that psychological well-being is enhanced by the freedom to engage with both masculine and feminine traits. Androgyny, therefore, may serve as a protective factor by allowing people to draw on a wider range of behavioral responses [44].

The relationship between gender traits and maladaptive behaviors (i.e., substance use) raises discussion on coping mechanisms associated with gender norms and roles. Zeldow et al. (1987) reported that femininity traits were negatively associated with drug involvement and frequency in a sample of males and females [23]. This aligns with gender role socialization theory [45], which states that feminine traits (i.e., sensitivity and compliance with social norms) reduce probability of risk-taking behaviors. The absence of a significant association between masculinity and drug involvement in the same study may reflect the normalization and social acceptance of maladaptive behaviors among people with masculine traits, leading to underreporting and consequently non-significant results.

Helgeson et al. (2022) found no significant association between masculinity traits and rehospitalization in a mixed-sex sample [27]. This is not entirely in keeping with previous findings of lesser health care utilization by men [46, 47], however, rehospitalization may engage other factors. Recent research has shown that people with high levels of neuroticism use health care more often, and also express behaviors that are not conducive to a healthy lifestyle (e.g., smoking and alcohol consumption) [48, 49]. Collectively, the results highlight the need to consider different gender attributes, including traits, and to incorporate personality characteristics into models to understand variations in service utilisation [48]. Future research would benefit from integrating intersectional approaches to capture both sex and gender attributes simultaneously to better understand how physiology, gender norms, and societal expectations interact among themselves and affect behaviors and function along the lifespan continuum. Such evidence is crucial to drive innovations in precision medicine and person-centered approaches to care.

Evolution of gender in research

By stratifying results by publication date (before 2016 or 2016 onwards; Fig. 3a and 3b, respectively), trends in the consideration of gender attributes in relation to functional outcomes became apparent. More recent studies demonstrated increased methodological rigor, including the use of validated gender measures, better conceptual clarity, and more sophisticated statistical approaches. Additionally, there has been greater attention to intersectionality and the role of cultural context in shaping gender norms and their influence on functional outcomes. These trends reflect a maturation of the field and a growing commitment to producing nuanced, theory-driven, and methodologically sound research.

Measurements and culture

The measures used to assess gender attributes and functional outcomes in samples of studies included in this review warrants critical appraisal, but falls outside the scope of this review. Nonetheless, it is important to bring attention to cultural and contextual relevance of the measures. Gender is deeply integrated in cultural norms, values, and the social structures [50] within which people live and work. The sociocultural context in which a measure was developed can significantly impact its applicability and interpretability [51, 52] if applied to different context or population. The studies included in our systematic review were conducted across the globe, including Australia, Canada, Ireland, Switzerland, Taiwan, United Kingdom, and United States (Table 1, 2). However, all measures used in these studies were developed in the United States, and their scores were validated in selective groups of people who were most often young, white, and highly educated. This raises important questions about cross-cultural validity and conceptual equivalence and applicability of these measures to different sociocultural backgrounds. Due to limited evidence, we were unable to systematically explore cross-cultural comparability. This remains a critical gap in evidence. Future validation research delving into understandings of how culture shapes the understanding and interpretation of gender attributes is greatly needed.

Implication for research, policy and practice

Several recommendations to guide future research have emerged from conducting this systematic review. The endorsement of the SAGER guideline resulted in knowledge dissemination regarding the value of sex and gender informed research. However, there remains relatively little scientific evidence on the topic of gender and none on the topic of sex. Also striking is the predominantly male samples that feature in the studies on this topic. This may be a function of the time and structural requirements of integration of the guidelines. It is likely that the research reported in this review was designed and conducted several years prior to publication when there was limited endorsement by publishers, and researchers’ raised concerns regarding false positives in subgroup testing in the presence of limited statistical power. Substantial opportunity exists to improve the integration of continuous metrics of sex and gender attributes in data collection, especially if including diverse participants in rehabilitation research concerning functional outcomes. The results would allow clinicians to identify sex- and gender-specific targets for enhancing functional outcomes in people who are diverse in their biological, social and gender-related characteristics, moving forward the evolution of the field of precision medicine and rehabilitation. This line of research is timely, particularly as research highlights variances stemming from biological sex-linked diversity, as well as biopsychosocial mechanisms driving differences in functionally relevant outcomes. This demonstrates yet again that people are diverse in both their biology and gender expression, and that prevention and care needs to be person-centered in order for it to be effective and meet the needs of all people.

The consolidation of evidence on the association between gender-related attributes of people and 12 functionally relevant outcomes reflects this diversity, with potential implications at the individual level (i.e., psychological well-being, perceived self-worth, coping mechanisms, and behaviors), community level (i.e., functional status and community integration), and healthcare system level (i.e., rehospitalization). The value of measuring gender attributes through existing standardized measures is of great importance to move beyond binary gender categorization of different sexes, as we observed great variability on the scores of the measures used in describing the samples. These results have implications for policy in service planning, including screening for gendered experiences and stress associated with pressure to meet expectations and socially acceptable roles, responsibilities and relationships in society, and the need to adopt trauma- and gender-informed care models attentive to biological diversity.

Study limitations

To be consistent with our protocol, results from all studies found in the five major databases were considered. Despite our best efforts to include all relevant studies, it is possible studies were missed if they were not indexed in the databases we searched.

With respect to the data that formed the basis for the review, its scientific value may be limited by a lack of assessment of the properties of the measures used in the reviewed studies. While relevant, this was outside the scope of this review and will be reported on separately.

All of the studies focused on gender attributes; the significance of associations with sex attributes (e.g., genetic, physiological, anatomical) were not reported. Thus, the role of sex individually and in association with gender was not available for review and discussion.

Finally, the inclusion of only English language articles with limited discussion of sociocultural context may affect the generalizability of our findings. Such data was not available for review.

There is a great deal of heterogeneity across the reviewed studies, limiting the certainty assessment to very low. None of the studies assessed the effect of sex attributes on the outcome of interest, which may be due to our application of a priori inclusion criteria requiring that measures of sex and gender attributes be used by at least two independent research teams. The challenges of dealing with substantive heterogeneity (i.e., in terms of the aims, methods, and focus of the gender attribute and the outcome studied in research included in this review) posed a problem for conducting meta-analysis [53]. Although the process of certainty assessment utilized was a practical way to generate the consistency and direction of associations between samples included in the review, it came with limitations due to the small number of studies, high heterogeneity in gender and functional outcomes, and the resultant very low certainty in the results. Controlling for covariates was not a common practice in research published prior to 2016. This brings up an important concern about evidence not accounting for intersecting strengths and vulnerabilities, where there is a possibility that other factors affected the gender attributes, and together they impacted on functional outcome. For example, the impact of age was not consistently considered in the reviewed studies. Where it was integrated into the analyses, significant associations between gender attributes and functional outcomes were found (Fig. 2). Age is a modulating factor in the context of sex and gender-based analysis, considering the physiological changes that occur with age and the changing social forces at play at different life stages.

It is imperative that new research on the topics of sex and gender consider the full complexity of these multidimensional constructs and the various intersecting effects, of which they are a part, that are at play in driving functional outcomes in diverse populations.

ATWS = Attitudes Toward Women Scale; BSRI = Bem Sex Role Inventory; BSRI-SF = Bem Sex Role Inventory - Short Form; CMNI = Conformity to Masculine Norms Inventory; CMNI-22 = Conformity to Masculine Norms Inventory - 22-item version; FIM = Functional Independence Measure; MBI = Maslach Burnout Inventory; ODI = Oswestry Disability Index; PAQ = Personal Attributes Questionnaire; RSES = Rosenberg Self-Esteem Scale; SWLS = Satisfaction with Life Scale; SF-36 = 36-Item Short Form Health Survey; SF-12 = 12-Item Short Form Health Survey

Ethics approach and consent to participate

We did not seek ethics approval, as this study did not involve primary data collection.

Consent for publication

All authors approved the final version of the manuscript and agree to be held accountable for all aspects of the work.

Availability of data and materials

The data and materials supporting the findings of this systematic review are available from the corresponding author upon reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the Global Brain Health Institute (GBHI), Alzheimer’s Association, and Alzheimer’s Society UK Pilot Award for Global Brain Health Leaders (GBHI ALZ UK-23-971123), and in part by Canada Research Chairs Program for Neurological Disorders and Brain Health (CRC-2021-00074). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors’ contributions

This review was conceptualized and designed by TM. Screening was independently performed by AB and AI. Quality appraisal, risk of bias assessment, and data extraction were performed by AB and AI. Data visualization was completed by TTS. TM prepared the initial draft of the manuscript. All authors critically reviewed drafts of the manuscript. All authors read and approved the final manuscript. Each author significantly contributed to the research, including conception, design, data acquisition, analysis, interpretation, drafting, or critical revision of the work.

Acknowledgements

We would like to thank trainees of the BRIDGE Lab (bridgelab.ca), Alicia Trista Ruetas, Anahita Nikkhou, Hyejun (Ashlee) Kim, Mursal Jahed, and Teodora Prnjat, for their support with title and abstract screening. We also acknowledge and sincerely thank the Library Services at the University Health Network for conducting the searches for this systematic review.

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No competing interests reported.

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Beyond Biology: A Systematic Review of the Effects of Gender on Functional Outcomes

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Abstract

Figures

Plain language summary

Highlights

1. Introduction

2. Methods

2.1. Protocol and registration

2.2. Search strategy

2.3. Eligibility criteria based on PICOS framework

2.4. Inclusion and exclusion criteria

2.5. Study selection process

2.6. Data extraction

2.7. Data synthesis

2.8. Quality and risk of bias assessment

2.9. Sensitivity analysis

2.10. Certainty assessment

2.11. Missing data

3. Results

3.1. Search results

3.2. Study characteristics

3.3. Attributes of sex assessments

3.4. Attributes of gender assessments

3.5. Outcome assessment

3.6. Relationship between gender attributes and outcomes

3.7. Sensitivity analysis

3.8. Risk of bias and certainty of evidence

3.9. Missing data

4. Discussion

5. Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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