Understanding Safety Culture in Long-Term Care
Bibliographic record
Abstract
OBJECTIVES: This case study aimed to understand safety culture in a high-risk secured unit for cognitively impaired residents in a long-term care (LTC) facility. Specific objectives included the following: diagnosing the present level of safety culture maturity using the Patient Safety Culture Improvement Tool (PSCIT), examining the barriers to a positive safety culture, and identifying actions for improvement. METHODS: A mixed methods design was used within a secured unit for cognitively impaired residents in a Canadian nonprofit LTC facility. Semistructured interviews, a focus group, and the Modified Stanford Patient Safety Culture Survey Instrument were used to explore this topic. Data were synthesized to situate safety maturity of the unit within the PSCIT adapted for LTC. RESULTS: Results indicated a reactive culture, where safety systems were piecemeal and developed only in response to adverse events and/or regulatory requirements. A punitive regulatory environment, inadequate resources, heavy workloads, poor interdisciplinary collaboration, and resident safety training capacity were major barriers to improving safety. CONCLUSIONS: This study highlights the importance of understanding a unit's safety culture and identifies the PSCIT as a useful framework for planning future improvements to safety culture maturity. Incorporating mixed methods in the study of health care safety culture provided a good model that can be recommended for future use in research and LTC practice.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".