Are new workers at elevated risk for work injury? A systematic review
Bibliographic record
Abstract
OBJECTIVE: To identify, appraise and synthesise studies that have examined the degree to which new workers are at an elevated risk of work-related acute injuries and musculoskeletal (MSK) injuries. METHOD: We searched three relevant electronic databases for studies published between 1995 and early 2018. Fifty-one studies using multivariate analyses met our relevance and quality appraisal criteria. These studies examined two different work outcomes: acute injuries (eg, cuts, burns and falls) and MSK injuries (eg, repetitive strain). RESULTS: In four of six studies looking at acute work injuries, new workers were found to be at an elevated risk of injury (ie, moderate supportive evidence of new worker risk). In another six studies looking at MSK symptoms, injuries or disorders, evidence of an elevated risk among new workers was insufficient or limited. CONCLUSIONS: Our review has potential implications for the prevention of work injuries, providing policy-makers and workplace parties with supportive evidence about the importance of prevention efforts focused on new workers, such as developing workplace policies that emphasise hazard exposure reduction, hazard awareness, hazard protection and worker empowerment.
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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".