Characterizing violence in health care in British Columbia
Bibliographic record
Abstract
BACKGROUND: The high rate of violence in the healthcare sector supports the need for greater surveillance efforts. AIM: The purpose of this study was to use a province-wide workplace incident reporting system to calculate rates and identify risk factors for violence in the British Columbia healthcare industry by occupational groups, including nursing. METHODS: Data were extracted for a 1-year period (2004-2005) from the Workplace Health Indicator Tracking and Evaluation database for all employee reports of violence incidents for four of the six British Columbia health authorities. Risk factors for violence were identified through comparisons of incident rates (number of incidents/100,000 worked hours) by work characteristics, including nursing occupations and work units, and by regression models adjusted for demographic factors. RESULTS: Across health authorities, three groups at particularly high risk for violence were identified: very small healthcare facilities [rate ratios (RR) = 6.58, 95% CI =3.49, 12.41], the care aide occupation (RR = 10.05, 95% CI = 6.72, 15.05), and paediatric departments in acute care hospitals (RR = 2.22, 95% CI = 1.05, 4.67). CONCLUSIONS: The three high-risk groups warrant targeted prevention or intervention efforts be implemented. The identification of high-risk groups supports the importance of a province-wide surveillance system for public health planning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".