Occupational and Mental Health Consequences of Women's Experiences of Gender Discrimination and Negative Workplace Acts in the Legal Profession
Bibliographic record
Abstract
Women have been entering the legal profession increasingly over the last three decades, yet continue to leave the profession at significantly greater rates than men. Previous research has documented the influence of workplace discrimination in influencing women's entry and retention in the legal profession, but less research attention has been directed toward the occupational and mental health consequences for these women. The present study examined whether negative acts in the workplace, gender discrimination, and work-life conflict were associated with negative occupational and mental health consequences for women lawyers. Questionnaires containing measures of negative acts in the workplace, gendered elements of the work environment, work-family conflict, job satisfaction, burnout, and mental health symptoms were mailed to a stratified random sample of Canadian women lawyers, resulting in a sample of 277 women lawyers from Alberta, Ontario, New Brunswick, and Nova Scotia. Findings supported our hypothesis that negative workplace experiences, including gender discrimination, would predict lower levels of job satisfaction and greater levels of burnout in women lawyers. In terms of mental health outcomes, work-life conflict emerged as the strongest predictor of perceived stress and anxiety, while negative workplace acts significantly predicted depression. The findings of this study highlight the importance of addressing negative acts and gender discrimination in the legal profession in order to increase women's job satisfaction and to safeguard their health and well-being.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".