Reconceptualizing Patient Safety Beyond Harm
Bibliographic record
Abstract
BACKGROUND: Although patients' and care partners' perspectives on patient safety can guide health care learning and improvements, this information remains underutilized. Efforts to leverage this valuable data require challenging the narrow focus of safety as the absence of harm. PURPOSE: The purpose of this study was to gain a broader insight into how patients and care partners perceive and experience safety. METHODS: We used a mixed-methods approach that included a literature review and interviews and focus groups with patients, care partners, and health care providers. An emergent coding schema was developed from triangulation of the 2 data sets. RESULTS: Two core themes-feeling unsafe and feeling safe-emerged that collectively represent a broader view of safety. CONCLUSION: Knowledge from patients and care partners about feeling unsafe and safe needs to inform efforts to mitigate harm and promote safety, well-being, and positive outcomes and experiences.
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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 | 0.127 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".