Gender roles in relation to symptoms of anxiety and depression among students and workers
Bibliographic record
Abstract
Background and objectives: Anxiety and depression are prevalent psychopathologies that affect twice as many women than men. Although the role of biological factors has been investigated, it has been argued that gender roles – defined by the feminine and masculine characteristics that society attributes to men and women – should also be considered. Gender roles are dynamic and shaped by life experiences. To date, most studies investigating the impact of gender roles on depressive and anxiety symptoms have recruited students. Here, we examined the impact of gender roles on depression and anxiety in healthy students and workers.Methods: Pooled data combining the Bem Sex-Role Inventory, State and Trait Anxiety Inventory and Beck Depression Inventory-II from 108 students (50 men) and 151 workers (75 men) aged 18–65 years old were analyzed. Gender roles were operationalized using continuous and categorical methods.Results: Higher masculinity predicted lower anxiety and depressive symptoms. The relationship between masculinity and anxiety was however only present for students. Higher feminity was associated with higher anxiety and lower depressive symptoms, and these relationships were not moderated by the student/worker status.Conclusion: Gender roles may relate to mental health differently according to the student/worker status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".