Safety culture in healthcare: mixed method study
Bibliographic record
Abstract
PURPOSE: Healthcare providers' perceptions of management's effectiveness in achieving safety culture improvements are low, and there is little information in the literature on the subject. Objective: The overall aim of this study was to examine the patient safety culture within an interprofessional team - physicians, nurses, nurse technicians, speech therapist, psychologist, social worker, administrative support - practicing in an advanced neurology and neurosurgery center in Southern Brazil. DESIGN/METHODOLOGY/APPROACH: The authors applied the safety attitudes questionnaire (SAQ) in a mixed methods study, with a quan→QUAL sequential explanatory approach. FINDINGS: In the quantitative phase, the authors found a negative safety climate through the SAQ. In the qualitative phase, the approach enabled participants to identify specific safety problems. For that, participants proposed improvements that were directly and quickly implemented in the workplace during the study. The joint analysis of the quantitative and qualitative data inferred that the information and reflections of the focus group participants supported and validated the SAQ statistical analysis results. This integrated approach illustrated the importance of various safety culture aspects as a multifaceted phenomenon related to healthcare quality. ORIGINALITY/VALUE: This study provides explanations for why management is associated negatively with safety climate in healthcare institutions. In addition, the study provides a novel contribution adding value to mixed methods research methodology.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".