Occupational and environmental risk factors for falls among workers in the healthcare sector
Bibliographic record
Abstract
Falls are a leading cause of occupational injury for workers in healthcare, yet the risk factors of falls in this sector are understudied. Falls resulting in workers' compensation for time-loss from work from 2004-2007 for healthcare workers in British Columbia (BC) were extracted from a standardised incident-reporting database. Productive hours were derived from payroll data for the denominator to produce injury rates; relative risks were derived through Poisson regression modelling. A total of 411 falls were accepted for time-loss compensation. Compared to registered nurses, facility support workers (risk ratio (95% CI) = 6.29 (4.56-8.69)) and community health workers (6.58 (3.76-11.50)) were at high risk for falls. Falls predominantly occurred outdoors, in patients' rooms and kitchens depending on occupation and sub-sector. Slippery surfaces due to icy conditions or liquid contaminants were a leading contributing factor. Falls were more frequent in the colder months (January-March). The risk of falls varies by nature of work, location and worker demographics. The findings of this research will be useful for developing evidence-based interventions. STATEMENT OF RELEVANCE: Falls are a major cause of occupational injury for healthcare workers. This study examined risk factors including occupation type, workplace design, work setting, work organisation and environmental conditions in a large healthcare worker population in BC, Canada. The findings of this research should contribute towards developing evidence-based interventions.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".