Attitudes and Behaviours of Hospital Pharmacy Staff toward Near Misses
Bibliographic record
Abstract
Near misses may be early warning signals for errors. The purpose of this study was to examine the attitudes and behaviours of Manitoba hospital pharmacists and technicians toward near misses and reporting. A web-based survey of pharmacy staff at hospitals (all have non-punitive paper-based incident reporting systems) was conducted in 2009. Survey respondents were asked via a validated survey about experience with and attitudes and behaviours toward near misses. Factor analysis and Cronbach's α were used to determine internal consistency reliability. Differences between pharmacists and technicians were compared using Fisher's exact test for categorical data and t tests for survey scales. Of 37 hospitals, one large tertiary care hospital declined to participate. Of approximately 500 pharmacy staff, 122 (24%) responded. The majority (54.1%) were pharmacists, and most worked in Winnipeg (73.8%). The majority of respondents (62% overall--48% of technicians and 73% of pharmacists (p=.008)--had experienced at least one near miss within the previous three months. However, only 27% had reported a near miss with occurrence-reporting forms. There was no difference in the reporting behaviours scale (eight items, Cronbach's α=.824) between pharmacists and technicians (pharmacist score 30.9 ± 4.8, technician score 29.6 ± 6.0; p=.215). There was no difference in the attitudes scale (23 items, Cronbach's α=.873) between pharmacists and technicians (pharmacist score 81.9 ± 9.4, technician score 80.2 ± 10.6; p=.388). We observed similar behaviours and attitudes between hospital pharmacists and technicians, although reporting of near misses was low. Education of pharmacy staff and managers about near misses may help to encourage reporting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".