A 5‐facet framework to describe patient engagement in patient safety
Bibliographic record
Abstract
BACKGROUND: Health care remains unacceptably error prone. Recently, efforts to address this problem have included the patient and their family as partners with providers in harm prevention. Policymakers and clinicians have created patient safety strategies to encourage patient engagement, yet they have typically not included patient perspectives in their development or been comprehensively evaluated. We do not have a good understanding of "if" and "how" patients want involvement in patient safety during clinical interactions. OBJECTIVE: The objective of this study was to gain insight into patients' perspectives about their knowledge, comfort level and behaviours in promoting their safety while receiving health care in hospital. METHODS: The study design was a descriptive, exploratory qualitative approach to inductively examine how adult patients in a community hospital describe health-care safety and see their role in preventing error. RESULTS: The findings, which included participation of 30 patients and four family members, indicate that although there are shared themes that influence a patient's engagement in safety, beliefs about involvement and actions taken are varied. Five conceptual themes emerged from their narratives: Personal Capacity, Experiential Knowledge, Personal Character, Relationships and Meaning of Safety. DISCUSSION: These results will be used to develop and test a pragmatic, accessible tool to enable providers a way to collaborate with patients for determining their personal level and type of safety involvement. CONCLUSION: The most ethical and responsible approach to health-care safety is to consider every potential way for improvement. This study provides fundamental insights into the complexity of patient engagement in 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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".