Understanding Safety Culture in Long-Term Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it