Assessing a safety climate tool adapted to address respiratory illnesses in Canadian hospitals
Bibliographic record
Abstract
Background: Studies have shown an association between workplace safety climate scores and patient outcomes. This study aimed to investigate (1) performance of the hospital safety climate scale that was adapted to assess acute respiratory illness safety climate, (2) factors associated with safety climate scores, and (3) whether the safety scores were associated with following recommended droplet and contact precautions. Methods: A survey of Canadian healthcare personnel participating in a cohort study of influenza during the 2010/2011-2013/2014 winter seasons. Factor analysis and structural equation modeling were used for analyses. Results: Of the 1359 participants eligible for inclusion, 88% were female and 52% were nurses. The adapted items loaded to the same factors as the original scale. Personnel working on higher risk wards, nurses, and younger staff rated their hospital's safety climate lower than other staff. Following guidelines for droplet and contact precautions was positively associated with ratings of management support and absence of job hindrances. Conclusion: The adapted tool can be used to assess hospital safety climates regarding respiratory pathogens. Management support and the absence of job hindrances are associated with hospital staff's propensity and ability to follow precautions against the transmission of respiratory illnesses.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".