Risks and causes of musculoskeletal injuries among health care workers
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal injuries (MSIs) persist as the leading category of occupational injury in health care. Limited evidence exists regarding MSIs for occupations other than direct patient care providers. An evaluation of the risks, causes and activities associated with MSIs that includes non-patient care health care occupations is warranted. AIMS: To examine the risks and causes of time-loss MSIs for all occupations in health care. METHODS: Workers employed by a health region in British Columbia were followed from April 2007 to March 2008 using payroll data; injuries were followed using an incidence surveillance database. Frequency and rates were calculated for all occupational injuries and MSIs and relative risks (RRs) were computed using Poisson regression. Causes and occupational activities leading to MSIs were tabulated for direct care occupations and non-patient care occupations. RESULTS: A total of 944 injuries resulting in time-loss from work were reported by 23 742 workers. Overall, 83% injuries were musculoskeletal. The two occupations showing highest RR of MSIs relative to registered nurses were facility support service workers [RR = 3.16 (2.38-4.18), respectively] and care aides [RR=3.76 (3.09-4.59)]. For direct patient care occupations, the leading causes of MSIs were awkward posture (25%) and force (23%); for non-patient care occupations were force (25%) and slip/fall (24%). Patient handling activities accounted for 60% of all MSIs for direct care occupations. For non-patient care occupations, 55% of MSIs were due to material/equipment handling activities. CONCLUSIONS: Prevention efforts for MSIs should be directed to non-patient care occupations as well and consider their occupation-specific causes and activities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".