Occupational Injuries and Their Determinants Among Healthcare Workers in Western Countries: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Healthcare workers (HCWs) in developed countries can be exposed to a wide range of hazards. The systematic identification of working conditions associated with the risk of occupational injury can significantly reduce this risk. METHODS: From January 2000 to December 2021, a scoping review was performed using PCC (Population, Concept, and Context) criteria and searching major scientific databases. Studies conducted in Western Countries, defined as member countries of the Organisation for Economic Co-operation and Development (OECD), were selected. RESULTS: We identified 282 studies for the present review. Studies focused more frequently on biological injuries (59%). Musculoskeletal injuries and injuries due to aggression and violence followed, based on the frequency of the investigated topic. CONCLUSIONS: Most studies focused on the risk of bloodborne infections, while a knowledge gap emerged on the epidemiology of accidental exposure to other transmission pathways. Although the proportion of injured workers is not negligible in most studies, the most common determinants and risk factors of injury are entirely preventable.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".