Occupational health and safety issues affecting young workers: A literature review
Bibliographic record
Abstract
Many overview articles, reports, book chapters and literature reviews have examined the health and safety of young workers. These sources discuss the relationships between the work conditions of young workers and the various indicators of accidents and occupational diseases. Breslin et al. [12,13] conducted two literature reviews of quantitative studies to determine which factors best predicted work accidents and occupational disorders in young people. The present article proposes a review of young people's occupational health and safety (OHS) factors (e.g., demographic, individual, professional, organizational, temporal and operational factors) in both qualitative and quantitative studies. Five types of problems were analyzed in greater depth, namely MSD symptoms, respiratory, allergy and toxicological problems, mental health and well-being, alcohol and drug consumption, and fatigue. This review likewise examines related dimensions that allow us to adopt a more global perspective on this subject by considering such elements as young people's values, their knowledge and attitudes, safety practices in companies, the safe integration of young people, and rehabilitation. A total of 189 scientific articles were selected on the basis of certain criteria. These articles came from refereed OHS journals published between 1994 and 2005.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".