Employee Absenteeism Based on Occupational Health Visits in an Urban Tertiary Care Canadian Hospital
Bibliographic record
Abstract
(1) To merge Occupational Health (OH) and Human Resources (HR) administrative data to describe reasons for absenteeism among hospital employees and (2) to consider the advantages and disadvantages of using these combined data for surveillance of health care workers. This study utilized a retrospective cohort design, involving a record linkage of two administrative databases at a Canadian general hospital: OH and HR. Data were included for the period of June 1, 2004, to May 31, 2005. Data linkage was performed using sex, postal code, and date of birth. The most common self-reported reasons for absence were respiratory illness (31%), gastrointestinal illness (17%), and musculoskeletal injuries/disabilities (15%). Employees working in the Department of General Medicine experienced the highest number of times absent--1.9 per 1,000 work hours. The department with the highest percentage of staff not reporting to OH was General Medicine (43%). This research highlights the issue of absenteeism among health care workers and the need to improve reporting of illness and injury to OH for surveillance efficacy. Further, a public health surveillance system that monitors OH visits among health care workers can facilitate public health practice.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".