Safe and shealthy integration into semiskilled jobs: does gender matter?
Bibliographic record
Abstract
Women report fewer injuries than men when they are young, but report a higher proportion of professional diseases later in life (35 years +). Understanding early work conditions that lead to postponed health outcomes is necessary if we are to prevent women's injury as well as men's. Introduction to work and safe integration programs are often put forward to prevent injuries among youth. This paper aims to illustrate some differences in the job introduction / integration process for men and women and to discuss some gender-based Occupational Health and Safety (OHS) implications. Data come from two waves of interviews with 31 students enrolled in vocational training for a semiskilled trade. Results demonstrated differences between men and women: trades chosen, OHS complaints, supervising patterns, integration to work, etc. Women often choose customer service jobs, have less formal supervision and are often left to themselves when learning to do their job. Men declared more accidents while women reported more musculoskeletal symptoms. Findings from this research suggest young women's OHS issues should not be trivialized and that specific youth prevention programs for sectors where women are more present, such as customer service, should be developed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".