Exploring perceptions of the effect of psychosocial hazards on workers' mental health
Bibliographic record
Abstract
Background: Previous literature has demonstrated that mining present significant hazards to workers (ILO, 2010, Gyekye, 2003, Amponsah-Tawiah et al, 2013) both mentally and physically. This presentation will consider some of the more controllable psychosocial hazards, defined by Leka and Cox (2010), in a population of mining workers with the additional objectives; 1) better understanding the relationship between the various hazardous factors, and 2) suggesting what could be targeted to improve workers’ mental health and safety from a mental health promotion point of view. Methods: Using qualitative methodologies (focus groups and individual interviews), a heterogeneous sample of participants (n=31) were recruited. These participants were chosen using random sampling strategy from a mining company in Ontario, Canada. A thematic analysis was used to explore perceptions of the effect of psychosocial hazard on workers' mental health. Findings: Work schedule, rotation, and shiftwork were listed among priorities highlighted by the workers to improve their mental health and well-being. Shiftwork was identified as a major occupational risk as well as a significant influence on work-family balance. Discussion: Some degree of control and autonomy over work schedules may prevent or mitigate deleterious occupational health outcomes and positively influence family and community life. The presentation will offer commentary on the usefulness of qualitative methodologies in occupational health research to improve health promotion strategies.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".