Patient safety climate strength: a concept that requires more attention
Bibliographic record
Abstract
BACKGROUND: When patient safety climate (PSC) surveys are used in healthcare, reporting typically focuses on PSC level (mean or per cent positive scores). This paper explores how an additional focus on PSC strength can enhance the utility of PSC survey data. SETTING AND PARTICIPANTS: 442 care providers from 24 emergency departments (EDs) across Canada. METHODS: We use anonymised data from the Can-PSCS PSC instrument collected in 2011 as part of the Qmentum accreditation programme. We examine differences in climate strength across EDs using the Rwg(j) and intraclass correlation coefficients measures of inter-rater agreement. RESULTS: Across the six survey dimensions, median Rwg(j) was sufficiently high to support shared climate perceptions (0.64-0.83), but varied widely across the 24 ED units. We provide an illustrative example showing vastly different climate strength (Rwg(j) range=0.17-0.86) for units with an equivalent level of PSC (eg, climate mean score=3). CONCLUSIONS: Most PSC survey results focus solely on climate level. To facilitate improvement in PSC, we advocate a simple, holistic safety climate profile including three metrics: climate level (using mean or per cent positive climate scores), climate strength (using the Rwg(j), or SD as a proxy) and the shape of the distribution (using histograms to see the distribution of scores within units). In PSC research, we advocate paying attention to climate strength as an important variable in its own right. Focusing on PSC level and strength can further understanding of the extent to which PSC is a key variable in the domain of patient safety.
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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 | 0.043 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".