Not Another Safety Culture Survey': Using the Canadian Patient Safety Climate Survey (Can-PSCS) to Measure Provider Perceptions of PSC Across Health Settings. Copyright: Creative Commons License.
Bibliographic record
Abstract
Full text of the Measure is in Data Supplement 2 (Table). BACKGROUND: The importance of a strong safety culture for enhancing patient safety has been stated for over a decade in healthcare. However, this complex construct continues to face definitional and measurement challenges. Continuing improvements in the measurement of this construct are necessary for enhancing the utility of patient safety climate surveys (PSCS) in research and in practice. This study examines the revised Canadian PSCS (Can-PSCS) for use across a range of care settings. METHODS: Confirmatory factor analytical approaches are used to extensively test the Can-PSCS. Initial and cross-validation samples include 13 126 and 6324 direct care providers from 119 and 35 health settings across Canada, respectively. RESULTS: Results support a parsimonious model of direct care provider perceptions of patient safety climate (PSC) with 19 items in six dimensions: (1) organisational leadership support for safety; (2) incident follow-up; (3) supervisory leadership for safety; (4) unit learning culture; (5) enabling open communication I: judgement-free environment; (6) enabling open communication II: job repercussions of error. Results also support the validity of the Can-PSCS across a range of care settings. CONCLUSIONS: The Can-PSCS has several advantages: (1) it is a theory-based instrument with a small number of actionable dimensions central to the construct of PSC; (2) it has robust psychometric properties; (3) it is validated for use across a range of care settings, therefore suitable for use in regionalised health delivery systems and can help to raise expectations about acceptable levels of PSC across the system; (4) it has been tested in a publicly funded universal health insurance system and may be suitable for similar international systems.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".