Dutch translation and validation of the Stanford Gender-Related Variables for Health Research questionnaire: Associations with lifestyle and mental health

Abstract

This study aimed to validate the Dutch version of the Stanford Gender-Related Variables for Health Research (GVHR) questionnaire and explore sex differences in lifestyle factors, mental health, and health status. In 2021, 569 Dutch participants (54% women, 45% men, aged 20–80) completed the survey. Sex-stratified analyses examined associations with lifestyle (obesity, smoking, alcohol use, physical activity), mental health (depression, anxiety, stress), and overall health status. A seven-factor model best fit the data, revealing significant gendered differences. Women reported higher caregiver strain, discrimination, and emotional intelligence, while men reported more social support and risk-taking. In women findings were more pronounced, and caregiving strain was linked to psychological distress, whereas emotional intelligence and social support were protective. For men, gender discrimination was associated with smoking, depression, anxiety, stress, and poorer health status. The GVHR effectively assesses gender-related behaviors in Dutch samples, though further validation is needed in more diverse populations.

Introduction

Sex and gender can differentially affect health outcomes (Kroenke et al., 2001; Mauvais-Jarvis et al., 2020). Whereas sex is defined as biological correlates of women and men, gender is a broad term which reflects how society and cultural norms interact with personal roles and beliefs to create behaviors. Multiple approaches exist in the operationalization of gender (Tadiri et al., 2021). Several studies have shown that individual differences in physical and mental health may be explained by gender, different from biological sex, irrespective of the operationalization of gender. For example, not being a primary earner, and women working in a female-dominated occupation were related to a lower cardiovascular disease risk according to the SCORE algorithm in the multiethnic HELIUS cohort (Bolijn et al., 2021). Moreover, a gender index score, predicting biological sex, reflecting traditionally “feminine” characteristics was associated with higher somatic symptoms burden and chronic diseases, especially in men (Ballering et al., 2020). In cardiac patients three gender constructs were distinguished (i.e. gender identity, traits, and sociocultural norm scores), showing that psychological distress was more common in patients scoring toward higher femininity on identity, traits, and socio-cultural norms (van den Houdt et al., 2024). In the general population trait masculinity (BSRI) was recently shown to be associated with lower psychological distress, independently of sex (Mommersteeg et al., 2024).

While above summarized results demonstrate the emerging association of gender with (mental) health outcomes, some of the studied variables and composite scores are difficult to intervene on. So, to create gender-related health interventions, aspects of gender that are related to behavior could provide potential starting points for intervention. With this in mind, Nielsen et al. (2021) developed the Stanford Gender-Related Variables for Health Research (GVHR) questionnaire. They reviewed, extracted, and validated items on behavioral aspects of gender (gender norms), intrapersonal gender-related traits (gender traits), as well as interpersonal gender-relations (gender-relations). Core variables represented gender norms, related to time spent on work and caregiving responsibilities as well as psychological strain attached to these gender norms. Gender related traits were focused on behaviors like risk taking, independence, and emotional intelligence. Gender-relations included questions on social support and discrimination experienced due to gender. Seven factors were identified, and results were validated and cross validated in three separate US samples (Nielsen et al., 2021).

Further validation of these gendered behaviors within other populations will facilitate research into gendered behaviors and can add to potential specific interventions within other populations. This is relevant as many outcomes, including classic cardiovascular risk factors related to lifestyle and psychosocial factors related to mental health and health status, remain to be examined from a more detailed sex and gender perspective. This is also the case in the Netherlands (Visseren et al., 2021). The current study aims to validate the Dutch translation of the Stanford GVHR in women and men between 20 and 80 years from the Dutch general population by performing a confirmatory factor analysis, and correlational analyses. We hypothesize that the GVHR subscale scores differ between women and men. Moreover, we expect the sex-stratified GVHR scores to be associated with behavioral lifestyle factors (presence of obesity, current smoking, any alcohol use, physical activity), mental health (depressive symptoms, anxiety, perceived stress), and health status or health-related quality of life (mental and physical health status).

Materials and methods

Questionnaire translation

Translation of the Stanford GVHR was performed according to the COSMIN guidelines adapted after Beaton et al. (2000), described in more detail by the VUMC document (VUMC, 2020). The questionnaire was translated from English to Dutch, separately by two scientists of Dutch speaking origin (PM, IvV) who were familiar with the concept, and independently by an official translation office to American-English by a Dutch native speaker (Taalcentrum-VU). Differences between the three versions were discussed with the original authors of the questionnaire, after which a preliminary translation was created. An official back-translation was done by a native English speaker, independent of the original English questionnaire. Interviews were held with 13 people (students and their relatives) to probe for interpretation of the items and answer categories. After discussion a final version with instruction was created. More details on the steps taken during the translation process are provided in the Supplemental File S1. Variable names, answer categories, coding per answer category, and notes on specific categories were added to the questionnaire. This information, as well as the Dutch version used is stored at Open Science Framework (OSF) (Mommersteeg et al., 2021). This study’s design and its analysis were not preregistered. Data are stored locally according to GDPR regulations and are available upon reasonable request.

Participants and design

Participants in the present study comprised a convenience sample from the general population. Data collection took place in February-March 2021 in the Netherlands. As part of a research course, 40 students approached 880 participants using quota sampling to have an equal number of women and men between 21 and 80 years in each age decile. Nonresponse was not recorded. The total questionnaire comprised about 200 items, including sociodemographic variables among which self-identified ethnicity and birth country, (chronic) conditions and health care visits, and validated questionnaires on psychological functioning, symptom perception, and gender. No financial incentive was rewarded. Local ethical approval was obtained (Tilburg University Ethics Review Board #RP55, December 8, 2020). In total 618 people (70%) filled out an online informed consent. Twenty persons did not consent to additional scientific analyses, and 29 persons did not fill out any items relevant for the present study, leaving 569 persons with (partially) completed questionnaires.

Gender and sex

Sociodemographics included gender (“Wat is uw geslacht?”) with options man, women, “other, please specify.” The GVHR questionnaire included two additional items on sex and gender, which were translated to Dutch and asked about sex assigned at birth (man, woman, intersex, “other, please specify …,” and “prefer not to state”), as well as current gender identification (man, woman, gender fluid/non-binary, “other, please specify …,” and “prefer not to state”).

Stanford Gender-Related Variables for Health Research (GVHR)

Our Dutch translated version of the GVHR consists of 28 items (Supplemental Table S1). Two items were added asking about hours of work or caregiving during the weekend spent on care or work in addition to during the week. Another item (GVHR09) asked about (informal) caregiving responsibilities (yes or no), which is not used for the sum-score calculation, but used for descriptive information. The remaining items were scored on a 1–5 scale (some reverse scored). All items were z-scored, and a mean calculation was performed for each subscale, based on these z-scores (Nielsen et al., 2021).

GVHR data cleaning

The GVHR questionnaire comprises of seven subscales; caregiver strain, work strain, risk-taking, independence, emotional intelligence, social support, and discrimination. Up to 58% of participants reported “not applicable” (0) on any caregiving item, and up to 27% on any work item. Consistent report of “not applicable” on all caregiving (32%) or work items (16%) were recoded as the lowest value possible (“never”  = 1), conform the original GVHR. In the calculated GVHR subscales there were between 12 and 17 missing cases (2%–3%).

Further examination of the data identified outliers, suggesting that interpretation of hours of work per weekday or weekend day did not always go well. One outlier in hours of caregiving per weekday (100 hours) was coded as missing. Moreover, if a person filled out that they spent on average 15 hours or more of work or caregiving per weekday (n = 54 and n = 25, respectively) or per weekend day (n = 6 and n = 22, respectively), the time spent on work/caregiving was divided by five workdays or two weekend days under the assumption that the given time was per week.

Sociodemographic factors

Sociodemographic factors were used to describe the sample and explore sex differences. Marital status, education level, work status, primary earner status, and household responsibilities were each categorized into three subgroups, potentially reflecting gendered differences between the sexes (Ballering et al., 2024; Mommersteeg et al., 2024; van den Houdt et al., 2024).

Lifestyle associated risk factors

Lifestyle associated risk factors were used as dichotomized primary outcome variables, and included self-reported body mass index (BMI), smoking status, alcohol use, and physical activity. BMI was calculated as kg/m² based on self-reported weight (kg) and height (m). A BMI ≥30 kg/m² was considered as “obese” (vs not obese). Smoking was dichotomized as current smoker versus no or former smoker. Any alcohol use was coded against no alcohol use. People who answered “agree” or “completely agree” to the statement “I make sure to get enough exercise” were coded as physically active (vs not active).

Mental health and health status

Mental health and health status were primary continuous outcome variables. These included depressive symptoms, anxiety, perceived stress, and mental and physical health status obtained via validated questionnaires. The physical health questionnaire (PHQ9) and the generalized anxiety disorder (GAD7) assess depressive symptoms and anxiety respectively, using a sumscore of nine items on a 0–3 scale, with a higher score indicating more symptoms (Kroenke et al., 2001; Lowe et al., 2008). The perceived Stress Scale (PSS) has 10 items on a 0–4 scale, a high sumscore indicates more perceived stress (Cohen et al., 1983). The short form 36 (SF36) derived 12 items were used to calculate physical and mental component summary score (PCS and MCS) with a higher score representing better physical and mental health status (Mols et al., 2009).

Statistical analyses

General descriptive information of the sample, stratified by sex, were examined by Chi-square tests for categorical and One-way ANOVA for continuous variables, with non-parametric tests when necessary.

To examine construct validity, we examined internal consistency using the McDonald’s Omega Total for the full dataset (Hayes and Coutts, 2020). We then carried out a confirmatory factor analysis. Previous research has suggested that there are seven factors within the GVHR questionnaire (Nielsen et al., 2021). Also those seven factors fall under three higher order constructs of gender norms, gender traits, and gender relations (Nielsen et al., 2021). Using the Lavaan package (Rosseel, 2012) in R, we ran a seven factor model. Second, we ran a model with three higher order factors, and correlated residuals to take the seven underlying factors into account. We used the CFI, TLI, RMSEA, and SRMR to gauge the fit of the models and compared the two models based off these fit indices.

An additional z-score per subscale was calculated for the GVHR to examine a radar plot, stratified by sex. One-way ANOVA was used to examine sex differences for the subscales. An explorative non-parametric correlation analysis (Spearman’s Rho) examined associations between the GVHR subscales.

Logistic regression analyses were used to explore the age-adjusted association of the GVHR subscales with dichotomized lifestyle associated risk factors obesity, smoking, alcohol use, and physical activity as outcome variables, stratified by sex. In a first step all seven subscales of the GVHR were entered, as well as age, for each outcome variable separately. Odds ratio’s (OR) with 95% confidence intervals (95% CI) were reported. Nagelkerke r ² was reported as an indicator of model fit, with a value of ≤0.2 indicating a weak association between the variables and the outcome, a value of 0.2–0.4 a moderate association, and a value of ≥0.4 a strong association.

For the continuous mental health and health status variables depressive symptoms, anxiety, perceived stress, physical- and mental health status age-adjusted multivariate analyses were run, stratified for sex. Again, findings were stratified by sex, and in the first step all seven subscales of the GVHR and age were entered, and analyses were run for each continuous outcome variable separately. Standardized coefficients beta and explained variance r ² were reported.

Results

Sex, gender, and descriptives

In our study the majority of participants assigned their gender aligned with their sex. In total five persons assigned their sex or gender according to a broader definition. This included, “prefer not to state” on one out of the three items (n = 1), “other” on one out of three items but without further specification (n = 2), or non-binary with the option for “women” on the other two items (n = 2). Four people reported combinations without further specification which could not differentiate between a typo or broader self-identification. In total 13–15 people (2%–3%) did not fill out the GVHR translated items on birth sex or current gender despite the option “prefer not to state” being present. Because a broader gender definition did not provide a subgroup to analyze and we did not want to exclude people, participants were included as either woman or men according to their current gender or most consistent reported sex. In total 307 women (54%) and 262 men (46%) participated. As per sampling, no sex differences in age were present (Table 1). Women were more often divorced or widowed, only had elementary or high school, more often had parttime work and less often fulltime, and had more household responsibilities than men. Men on average had a higher BMI, but not more obesity, and men more often reported alcohol use compared to women. There was no significant sex difference in reported (informal) caregiving (28% women; 23% men). Women had more depressive symptoms, anxiety, and perceived stress, and a worse mental health status compared with men. In total 91% of the participants self-identified as Dutch (n = 520), or Turkish (2.8%), European (2.4%), Surinam or Dutch-Caribbean (1.4%), Indonesian (0.5%), or other (1.2%), without significant sex differences (data not shown).

Table 1.

Descriptives stratified by sex.

Descriptives	N	Women		Men		Test value	p-Value
		54%	307	46%	262
Age	569	48.28	17.16	49.39	16.53	0.62	0.432
Sociodemographic factors
Marital status
Divorced/widowed	569	10%	31	5%	13	5.60	0.061
With partner		78%	238	80%	210
Single		12%	38	15%	39
Education level
Elementary or high school	568	22%	68	15%	40	5.14	0.076
Middle vocational training		32%	97	31%	82
College education or higher		46%	141	53%	140
Work status
Parttime	569	45%	139	13%	34	99.1	<0.001
Not working		34%	104	30%	78
Fulltime		21%	64	57%	150
Primary earner status
Not primary earner	569	57%	174	16%	41	139.4	<0.001
Equal earners		21%	65	15%	38
Primary earner		22%	68	70%	183
Household responsibilities
I do most (>60%)	569	64%	196	23%	61	119.0	<0.001
Shared		30%	92	41%	107
Partner/other does most		6%	19	36%	94
Current (informal) caretaking (yes vs no; item GVHR09)	556	28%	83	23%	58	1.83	0.176
Lifestyle associated risk factors
BMI (kg/m2)	566	25.06	4.51	26.02	4.16	6.77	0.010
Obesity (BMI ≥30)	566	13%	41	14%	36	0.04	0.851
Current smoker (yes)	569	14%	42	13%	34	0.06	0.806
Alcohol use (vs none)	569	81%	249	90%	235	8.20	0.004
Physically active (vs not active)	569	55%	170	56%	148	0.07	0.790
Mental health status
Depressive symptoms (PHQ9)	563	4.06	3.81	3.71	4.40	2.10 a	0.036
Anxiety (GAD7)	566	3.64	3.81	2.95	3.61	2.63 a	0.008
Perceived stress (PSS)	566	12.92	6.27	11.01	6.20	13.17	<0.001
Physical health status (PCS)	566	47.93	6.73	48.72	5.53	2.24	0.135
Mental health status (MCS)	566	38.71	6.00	39.99	5.72	6.67	0.010

^aDue to a skewed distribution, a Mann-Whitney test was used with a standardized U test value.Bold typeface indicates a significant difference, italics is p < .10

GVHR confirmatory factor analysis, sex differences, and correlations

In Supplemental File S1 steps and decisions taken in the translation process have been described in more detail. The confirmatory factor analysis was performed on 508 participants with complete data on all items. The Omega total was 0.85. The seven-factor model fit the data well (CFI: 0.934, TLI: 0.923, RMSEA = 0.04, SRMR = 0.060). The higher order three-factor model with correlated residuals had a worse fit (CFI: 0.811, TLI: 0.789, RMSEA: 0.068, SRMR = 0.085). The seven-factor model fit the data better, which was used in subsequent analyses.

Figure 1 depicts z-transformed GVHR subscales stratified by sex. The GVHR subscales independence and work strain were not significantly different between women and men. Women on average reported significantly more caregiver strain and higher emotional intelligence, whereas men reported more social support and risk taking compared to women. While the majority of people did not report any discrimination because of their gender, women experienced more discrimination than men. In total 33% (N = 98) of women and 16% (N = 42) of men reported having experienced any discrimination on any of the six items. Correlations between the GVHR subscales, stratified for sex, were modest (Supplemental Table S2). Explorative analysis showed that items on hours spent on caregiving or work on weekdays correlated modestly with hours during the weekend (Range of r = 0.3–0.6).

Figure 1.

Radar chart showing the distribution of z-scores for each GVHR subscale stratified by sex.

On average women and men did not differ in work strain and independence. Women reported more caregiver strain, discrimination, and emotional intelligence compared to men, whereas men showed more risk taking as well as social support compared to women.

**p < 0.01. ***p < 0.001.

GVHR subscales associated with lifestyle and mental health

Exploration of GVHR subscales showed that the subscales were more often significantly associated with lifestyle factors in women than in men (Table 2), albeit with limited model fit. In women, work strain and caregiver strain were related to more obesity. Work strain and higher risk taking were related to higher odds of alcohol use in women, whereas caregiver strain was associated lower odds of alcohol use. In both women and men, risk taking was related to physical activity. In men, but not in women, discrimination was associated with smoking.

Table 2.

Sex stratified age-adjusted logistic regression of lifestyle associated risk factors with GVHR gender subscales.

GVHR subscales and age stratified by sex	Obesity	Smoking	Alcohol use	Physical activity
	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Women
Age	1.04 (1.01–1.07)	0.97 (0.95–1.00)	1.03 (1.01–1.06)	1.03 (1.02–1.05)
Work strain	2.13 (1.21–3.73)	0.80 (0.46–1.39)	2.87 (1.67–4.94)	1.01 (0.68–1.48)
Caregiver strain	1.63 (1.11–2.38)	1.37 (0.96–1.96)	0.65 (0.46–0.91)	0.81 (0.61–1.08)
Emotional intelligence	1.10 (0.63–1.93)	0.83 (0.49–1.40)	1.12 (0.70–1.80)	1.32 (0.91–1.93)
Risk taking	0.80 (0.48–1.34)	0.90 (0.56–1.45)	1.80 (1.14–2.83)	1.67 (1.17–2.36)
Independence	0.83 (0.56–1.21)	1.17 (0.80–1.71)	0.85 (0.61–1.19)	1.18 (0.90–1.55)
Discrimination	0.86 (0.53–1.41)	1.15 (0.79–1.67)	1.33 (0.84–2.10)	0.98 (0.73–1.32)
Social support	0.96 (0.63–1.45)	0.69 (0.47–1.02)	0.95 (0.65–1.38)	1.00 (0.75–1.33)
Model fit: Nagelkerke r 2	0.13	0.07	0.14	0.12
Men
Age	1.04 (1.01–1.08)	0.98 (0.96–1.01)	1.00 (0.97–1.04)	1.01 (0.99–1.03)
Work strain	1.20 (0.62–2.31)	0.74 (0.40–1.39)	1.34 (0.65–2.76)	1.17 (0.75–1.81)
Caregiver strain	0.90 (0.43–1.87)	1.66 (0.92–2.97)	0.94 (0.47–1.89)	1.33 (0.84–2.12)
Emotional intelligence	1.00 (0.58–1.75)	0.80 (0.45–1.39)	0.61 (0.33–1.16)	0.97 (0.67–1.40)
Risk taking	1.07 (0.67–1.72)	1.09 (0.67–1.75)	1.69 (0.97–2.93)	1.43 (1.02–2.01)
Independence	1.14 (0.71–1.84)	0.96 (0.62–1.49)	0.81 (0.48–1.34)	1.26 (0.92–1.73)
Discrimination	1.07 (0.54–2.14)	1.65 (1.00–2.71)	0.60 (0.34–1.06)	0.98 (0.62–1.55)
Social support	1.52 (0.93–2.50)	1.07 (0.67–1.70)	0.91 (0.54–1.53)	0.90 (0.66–1.23)
Model fit: Nagelkerke r 2	0.09	0.07	0.07	0.06

Bold typeface indicates a significant odds ratio (OR) with 95% confidence interval.

The GVRH subscales were associated with several mental health and health status outcomes (Table 3), explaining 6%–24% of the variance. In women, caregiver strain, discrimination, low social support, low emotional intelligence, low risk taking, and independence were related to increased psychological distress as indicated by depressive symptoms, anxiety, and stress. Social support in women was related to better mental and physical health status, whereas caregiver strain was related to poor physical health status. Risk taking and independence were related to improved mental health status in women. In men, the most pronounced finding was that discrimination was consistently significantly related to more psychological distress and worse mental and physical health status. Furthermore, in men, social support was related to less depressive symptoms and stress, and risk taking was related to lower experienced stress and better mental and physical health status.

Table 3.

Multivariate analysis of mental health and health status with GVHR gender subscales, stratified by sex.

GVHR subscales and age stratified by sex	Depression	Anxiety	Stress	PCS	MCS
Women
Age	−0.326***	−0.315***	−0.302***	−0.146*	0.246**
Work strain	0.035	0.001	0.051	−0.040	−0.015
Caregiver strain	0.110*	0.207***	0.227***	−0.150**	−0.060
Emotional intelligence	−0.173**	−0.157**	−0.169**	0.095	0.109
Risk taking	−0.087	−0.148*	−0.182**	0.085	0.170**
Independence	−0.072	−0.046	−0.124*	<0.001	0.153**
Discrimination	0.193***	0.187***	0.164**	−0.084	−0.066
Social support	−0.189**	−0.173**	−0.200***	0.129*	0.162**
r 2 Model	20.7%***	20.7%***	24.4%***	9.1%**	12.6%***
Men
Age	−0.287***	−0.206**	−0.185*	−0.104	0.084
Work strain	−0.040	−0.010	−0.044	0.126	0.009
Caregiver strain	0.038	0.114	0.101	−0.062	−0.069
Emotional intelligence	−0.005	−0.038	−0.047	0.036	0.013
Risk taking	−0.076	−0.047	−0.179**	0.245***	0.143*
Independence	0.050	−0.042	−0.002	−0.066	−0.024
Discrimination	0.309***	0.299***	0.287***	−0.218***	−0.181**
Social support	−0.119*	−0.112	−0.176**	−0.075	0.091
r 2 Model	19.2%***	17.6%***	17.0%***	16.7%***	6.4%*

Standardized coefficient beta is reported.

PCS: physical component score, physical health status; MCS: mental component score, mental health status.

Bold typeface indicates a significant association with *p < 0.05. **p < 0.01. ***p < 0.001.

Discussion

In the present study, we aimed to translate and validate the GVHR questionnaire in a Dutch community sample of women and men, exploratively showing that scores on GVHR subscales differ between women and men associated with several lifestyle factors (presence of obesity, current smoking, any alcohol use, physical activity), mental health (depressive symptoms, anxiety, perceived stress), and health status (mental and physical health status).

Sex and gender

Despite the inclusion of three items asking about a person’s (birth) sex and gender, with more options than “woman” and “man,” this did not consistently provide a broad spectrum of variation in self-identification. Some of the (absence in) variation in the present data were possibly due to lack of filling out all items or typo’s. Asking about variation in sex and gender can be seen as diversity-sensitive, acknowledging more inclusive options (Stadler et al., 2023). However, it could also represent ‘Otherness’, or ‘moving others away/toward the in-group’, which can worsen psychological distress, pose a risk for unblinding, exposure, stigmatization, and discrimination (Bhugra et al., 2023). Given our modest sample size, we choose not to exclude a potentially diverse group. The GVHR was developed to be able to examine findings irrespective of someone’s sex or gender (Nielsen et al., 2021). We choose to examine sex/gender stratified findings in order to be able to attribute (differences in) findings associated with sex/gender (Peters and Woodward, 2023).

GVHR validation

In line with the original questionnaire, confirmatory factor analysis suggested the presence of seven GVHR subscales. The subscales were weakly correlated with one another, providing additional and separate information. We added hours during the weekend spend on care or work in addition to during weekdays, which was modestly correlated with hours reported during weekdays. The items on work and caregiving may be less suited for people who do not currently work or have caretaking responsibilities, which may bias certain age categories as well as healthy groups, and age-stratified groups could be examined separately.

The GVHR item “Are you currently responsible for taking care of someone in need?” (GVHR09), was filled out negatively in 75% of the sample, which is a challenge for the interpretation of the subsequent caregiving items. When combining the other caregiving items only 32% reported no caregiving consistently. These items may therefore not reflect informal caregiving but may be more broadly interpreted to include daily chores and caregiving responsibilities. Therefore, the caregiving subscale should be interpreted or used depending on the studied population, for example, in people with informal caregiving responsibilities, or more broadly.

The number of people reporting any discrimination for gender (33% in women and 16% in men) was severely skewed. In the probing phase it became clear that the original description “how often you have felt discriminated against because of your gender” was interpreted differently among different people. We added a descriptive sentence (Supplemental File S1) to aid recognition of gender discrimination; “By discrimination we imply that people treat you differently, disadvantage you, or exclude you based on whether you are man or woman.” In other studies, perceived gender discrimination numbers range widely between 3.4% and 67% (de la Torre-Pérez et al., 2022). In a scoping review, two key aspects of gender discrimination were identified; undervaluation (different recognition, opportunities in access, evaluation standards, and expectations) and different treatment (verbal abuse and behavior; de la Torre-Pérez et al., 2022). We advise to add a description to the discrimination items including both the undervaluation and different treatment for clarification. For example; “Gender discrimination can take many forms, and can include being undervalued because of your gender, or receiving different treatment because of your gender.”

Gender norms: Work strain and caregiving

Gender norms were characterized by work strain and caregiving strain. The work strain items comprised questions tapping into aspects of having to work fast, having repetitive tasks, and physical and mental exhaustion in addition to hours of work (Nielsen et al., 2021). Work strain did not show sex differences, nor was it associated with mental health status. However, in women it was related to a higher prevalence of obesity and alcohol use. Work strain has been found to be associated with high alcohol use in women (Bildt and Michélsen, 2002). Long working hours and workstrain, reported cross-sectionally, have been associated with obesity in both women and men (Niedhammer et al., 2021). The present findings are in contrast with job strain to be more prevalent in women, and work strain being associated with poor mental health (Bildt and Michélsen, 2002; Herrero et al., 2012; Niedhammer et al., 2021; Theorell et al., 2014).

Women reported significantly higher caregiving strain than men, which was associated to more obesity, reduced prevalence of alcohol use, and worse mental health. The findings on mental health are in line with studies in the general population, reporting psychological distress in women to be related to time spend on childcare, and in men related to time devoted to housework (Matud et al., 2015). A review on (informal) caregiving for people with mental disorders shows mixed findings, concluding that sex-differences may be small and unlikely to be of clinical significance (Sharma et al., 2016). Worse mental health was observed in unformal, unpaid caregivers (Bueno and Chase, 2023), both in women and men, however men were less often involved in informal caregiving (Ervin et al., 2022). Partner caregivers burden was more pronounced in women (Swinkels et al., 2017). In the present study no significance sex difference in the prevalence of (informal) caregiving was observed, and it should be kept in mind that the current caregiving strain subscale reflects a general caregiving burden.

Gender-related traits: Emotional intelligence, risk taking, and independence

Gender related traits showed sex-stereotyped differences with women reporting more emotional intelligence, men reporting more engagement in risk taking, whereas no sex differences were apparent for independence. Fernández-Abascal and Martín-Díaz (2015) observed that emotional intelligence was more strongly associated with mental health than physical health. In the present study emotional intelligence, expressed as talking to friends and expressing feelings was not significantly associated with either mental or physical health status, which is more in line with the findings by Nielsen et al. (2021). Moreover, we did not observe any association with lifestyle associated risk factors, whereas a meta-analysis has shown a negative association of emotional intelligence with health risk behaviors (Sánchez-López et al., 2022), including smoking, but not obesity (Nielsen et al., 2021). Emotional intelligence was beneficial for women in terms of having less depressive symptoms, anxiety, and stress, but for men it was neither beneficial nor a risk.

Risk taking, a combination of general, financial, and recreational risk taking, was higher in men and associated with more physical activity, less psychological distress, and improved health status. The improved health status is in line with Nielsen et al. (2021), though in contrast, we did not observe an association with smoking or obesity, and only in women was risk taking related to alcohol use. Studies have shown that over the life span, risk taking decreases, and men more often engage in risk taking behavior than women (Rolison et al., 2013). It could be relevant for future studies to examine these findings further stratified for age cohorts.

The independence subscale rates the importance being able to solve your problems on your own, and to be independent. No sex differences or associations with lifestyle factors were observed, which was in line with Nielsen et al. (2021). In women, but not in men, independence was related to lower stress and better mental health status. Overall, gender-related traits emotional intelligence, risk taking, and independence were more beneficial for women in terms of mental health than for men.

Gender relations: Social support and discrimination

Social support in the present study was higher in men compared to women, which contrasts findings that women have a larger support network and perceive more quality in their social support, or report more social support (Kneavel, 2021; Matud et al., 2015). Social support was operationalized as how often in the past year was there someone to show you love and affection, and someone to help you with daily chores, which taps into emotional and instrumental support in a quantitative way. Consistent with other studies, social support was beneficial for both women and men in terms of having less psychological distress and improved health status (Johansen et al., 2021; Kneavel, 2021; Matud, 2019; Matud et al., 2015), though no association with lifestyle factors were observed.

Discrimination because of gender was more often experienced by women compared to men, which is in line with other studies (Dambrun, 2007; de la Torre-Pérez et al., 2022). Consistent with other studies, in both women and men, discrimination was related to more depressive symptoms, anxiety, and stress (Dambrun, 2007; de la Torre-Pérez et al., 2022). Experiencing discrimination of gender was the strongest association with (mental) health observed in men; related to more smoking, more depressive symptoms, anxiety, stress, and worse health status. Dambrun (2007) found that perceived discrimination mediated the effect of gender differences on psychological distress in group of psychology students, which remains to be replicated in a more diverse cohort. The associations with smoking and worse health status seems in line with research papers that have found associations of discrimination with specific (disease) conditions, in ethnically diverse groups, work environment, or LGBTQI+ community (de la Torre-Pérez et al., 2022; Hazel and Kleyman, 2020; Kelly-Brown et al., 2022; Williams et al., 2019). While our analyses were limited by the relatively small number of people reporting any discrimination, we show that the GVHR questionnaire can provide a starting point for further examination of specific factors associated with perceived gender discrimination, be it health outcomes or etiological factors.

Other limitations include a bias in comparison to the general population, such that our sample received a higher education, and self-identified ethnic background was less diverse compared to the general population. Dichotomized outcomes are prone to reduced power to reliably detect differences, though a minimal sample size of 500 has been suggested to derive statistics that represent the parameters (Bujang et al., 2018). In the present, explorative study, stratified findings included about 300 women and 250 men, which limits interpretation of the dichotomous lifestyle associated risk factors. Moreover, lifestyle factors were self-reported, assuming that people may underestimate their weight, overestimate their length (Zhang et al., 2023), and not report on smoking. However, studies examining differences in self-reported versus measured smoking status and BMI showed high associations, though misreport was more prevalent in men (Hovanec et al., 2019; Zhang et al., 2023).

Conclusion

The Dutch version of the Stanford Gender-Related Variables for Health Research (GVHR) questionnaire is a valid assessment of seven subscales, which can be administered to explore gender-related behavioral health differences. While work strain and independence were not different, women report more caregiver strain, higher emotional intelligence, and more discrimination, and men report more social support and risk taking. Explorative findings were more pronounced for mental health and health status than for lifestyle associated findings, and more often observed in women than in men. In women, caregiver strain, discrimination, low social support, low emotional intelligence, low risk taking, and independence were related to increased psychological distress as indicated by depressive symptoms, anxiety, and stress. In men, the most pronounced finding was that discrimination was consistently significantly related to more psychological distress and worse mental and physical health status.