Epidemiology of occupational injury among cleaners in the healthcare sector
Bibliographic record
Abstract
BACKGROUND: The cleaning profession has been associated with multiple ergonomic and chemical hazards which elevate the risk for occupational injury. AIMS: This study investigated the epidemiology of occupational injury among cleaners in healthcare work settings in the Canadian province of British Columbia. METHODS: Incidents of occupational injury among cleaners, resulting in lost time from work or medical care, over a period of 1 year in two healthcare regions were extracted from a standardized operational database and with person-years obtained from payroll data. Detailed analysis was conducted using Poisson regression modeling. RESULTS: A total of 145 injuries were identified among cleaners, with an annual incidence rate of 32.1 per 100 person-years. After adjustment for age, gender, subsector, facility, experience and employment status, Poisson regression models demonstrated that a significantly higher relative risk (RR) of all injury, musculoskeletal injury and cuts was associated with cleaning work in acute care facilities, compared with long-term care facilities. Female cleaners were at a higher RR of all injuries and contusions than male cleaners. A lower risk of all injury and allergy and irritation incidents among part-time or casual workers was found. Cleaners with >10 years of experience were at significantly lower risk for all injury, contusion and allergy and irritation incidents. CONCLUSION: Cleaners were found to be at an elevated risk of all injury categories compared with healthcare workers in general.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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".