When healthcare workers get sick: Exploring sickness absenteeism in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: To determine the demographic and work characteristics of healthcare workers who were more likely to take sickness absences from work in British Columbia, Canada. METHODS: Payroll data were analyzed for three health regions. Sickness absence rates were determined per person-year and then compared across demographic and work characteristics using multivariate Poisson regression models. The direct costs to the employer due to sickness absences were also estimated. RESULTS: Female, older, full-time workers, long-term care workers and those with a lower hourly wage were more likely to take sickness absences and had similar trends with respect to the costs due to sickness absence. For occupations, licensed practical nurses, care aides and facility support workers had higher rates of sickness absence. Registered nurses, and those workers paid high hourly wages were associated with highest sickness related costs. CONCLUSION: It is important to understand the demographic and work characteristics of those workers who are more likely to take sickness absences in order to make sure that they are not experiencing additional hazards at work or facing detrimental workplace conditions. Policy makers need to establish healthy, safe and in turn more productive workplaces. Further research is needed on how interventions can reduce sickness absence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".