The health and safety of young people at work: a Canadian perspective
Bibliographic record
Abstract
Purpose To review recent research on work injuries among young workers, to examine efforts of Canadian authorities to reduce injury rates among this group, and to identify indicators of success. The information is useful in the design of public health programs or within organizations employing young workers, and identifies future research needs. Design/methodology/approach A range of recently published (1998‐2007) systematic and narrative reviews, research papers, government documents and websites, primarily from Canadian sources, was reviewed. Documents were critically reviewed to identify factors associated with increased risk of injury, and to examine the use of social marketing approaches in the prevention of injury. Findings Recent research tends to confirm that the types of jobs that young workers do, and the fact that they often have only short‐term experience in the job, are major factors contributing to the increased risk that young workers experience. Social marketing is being widely used in Canada as a prevention approach, but research on its effectiveness is in its infancy. Originality/value The paper summarises recent research on young worker safety, highlighting findings that are of value for program design and future research needs.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".