What Are the Impacts of Sexual Harassment in the Workplace on the Mental Health and Productivity of Women Working in Male-Dominated Industries in Canada? What Strategies Should They Use to Cope with the Harmful Impacts of This Stressor on Their Mental Health?
Bibliographic record
Abstract
Sexual harassment in the workplace is still omnipresent in the workplace in Canada.It is even more ubiquitous in male-dominated industries, and women holding these "masculine" positions are the principal victims.Sexual harassment can take many forms, such as physical, verbal, or even virtual.Many Canadian women working in male-dominated industries are victims of this type of harassment in the workplace, which causes them intense stress and substantial emotional distress.Whether it's in the Canadian Armed Forces, police, construction, farming, engineering, or firefighting, to name a few.Organizations should impose stricter policies, regulations, and sanctions against the harassers to ensure women's safety, security, and productivity.Women should use strategies to cope with the consequences of sexual harassment on their mental health in their workplace.This research will investigate the impacts of sexual harassment in the workplace on the mental health and productivity of women working in male-dominated industries in Canada.It will also provide recommendations and strategies that women can use to cope with the harmful impacts of this stressor on their mental health.How to cite this paper: Bekkai, D. (2024) What Are the Impacts of Sexual Harassment in the Workplace on the Mental Health and Productivity of Women Working in Male-Dominated Industries in Canada?What Strategies Should They Use to Cope with the Harmful Impacts of This Stressor on Their
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".