Work-related injury among direct care occupations in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: To examine how injury rates and injury types differ across direct care occupations in relation to the healthcare settings in British Columbia, Canada. METHODS: Data were derived from a standardised operational database in three BC health regions. Injury rates were defined as the number of injuries per 100 full-time equivalent (FTE) positions. Poisson regression, with Generalised Estimating Equations, was used to determine injury risks associated with direct care occupations (registered nurses [RNs], licensed practical nurses [LPNs) and care aides [CAs]) by healthcare setting (acute care, nursing homes and community care). RESULTS: CAs had higher injury rates in every setting, with the highest rate in nursing homes (37.0 injuries per 100 FTE). LPNs had higher injury rates (30.0) within acute care than within nursing homes. Few LPNs worked in community care. For RNs, the highest injury rates (21.9) occurred in acute care, but their highest (13.0) musculoskeletal injury (MSI) rate occurred in nursing homes. MSIs comprised the largest proportion of total injuries in all occupations. In both acute care and nursing homes, CAs had twice the MSI risk of RNs. Across all settings, puncture injuries were more predominant for RNs (21.3% of their total injuries) compared with LPNs (14.4%) and CAs (3.7%). Skin, eye and respiratory irritation injuries comprised a larger proportion of total injuries for RNs (11.1%) than for LPNs (7.2%) and CAs (5.1%). CONCLUSIONS: Direct care occupations have different risks of occupational injuries based on the particular tasks and roles they fulfil within each healthcare setting. CAs are the most vulnerable for sustaining MSIs since their job mostly entails transferring and repositioning tasks during patient/resident/client care. Strategies should focus on prevention of MSIs for all occupations as well as target puncture and irritation injuries for RNs and LPNs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".