The Impact of Multiple Marginalized Social Statuses: How Being a Sexual Minority, a Woman, or Living with Low Income Relates to Workers' Well‐being
Bibliographic record
Abstract
Abstract Limited research explores the well‐being of multiply marginalized workers. Aiming to illustrate the application of intersectionality‐inspired analysis to the fields of management and occupational health, we examined how being a sexual minority (non‐heterosexual), having low income, and identifying as a woman are associated with well‐being outcomes (e.g., impaired performance, troublesome symptoms, positive mental health). A survey was completed by 331 Québec workers. We used regression analysis to examine individual, additive, and interactive relationships between marginalized statuses and outcomes. Having multiple marginalized statuses was associated with impaired performance, troublesome symptoms and less positive mental health. The most negative outcomes were reported by low‐income gay or bisexual workers. Organizational policies and managers should consider intersecting identities to better support marginalized workers' 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".