Injury Rates, Predictors of Workplace Injuries, and Results of an Intervention Program Among Community Health Workers
Bibliographic record
Abstract
BACKGROUND: Few incidence studies of workplace injuries among community health workers exist, and evidence regarding the effectiveness of interventions in this population is lacking. OBJECTIVES: To determine the incidence of workplace injury among community health workers in British Columbia; to identify predictors of injury; and to assess the effectiveness of a multicomponent intervention program in this population. METHODS: Data were collected from an intervention study of 648 community health workers from six agencies to calculate injury rates. Interventions included an education and training module, a risk assessment tool and resource guide, and a lift equipment registry. RESULTS: The majority of injuries were attributed to overexertion and falls. Annual incidence rates were 20.7% for reported injuries, and 8.1% for time-loss injuries. A history of previous injuries and working full time were predictors of time to first injury report. Participants who received an intervention were significantly more likely to report workplace injuries than participants in the comparison group, but were less likely to incur a time-loss injury. CONCLUSIONS: The interventions used in this study led to increased awareness and an increase in reported injuries but resulted in fewer time-loss injuries. The mechanisms that led to these findings need to be explored further.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".