Patient safety culture: finding meaning in patient experiences
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to determine what patient and family stories can tell us about patient safety culture within health care organizations and how patients experience patient safety culture. DESIGN/METHODOLOGY/APPROACH: A total of 11 patient and family stories of adverse event experiences were examined in September 2013 using publicly available videos on the Canadian Patient Safety Insitute web site. Videos were transcribed verbatim and collated as one complete data set. Thematic analysis was used to perform qualitative inquiry. All qualitative analysis was done using NVivo 10 software. FINDINGS: A total of three themes were identified: first, Being Passed Around; second, Not Having the Conversation; and third, the Person Behind the Patient. Results from this research also suggest that while health care organizations and providers might expect patients to play a larger role in managing their health, there may be underlying reasons as to why patients are not doing so. PRACTICAL IMPLICATIONS: The findings indicate that patient experiences and narratives are useful sources of information to better understand organizational safety culture and patient experiences of safety while hospitalized. Greater inclusion and analysis of patient safety narratives is important in understanding the needs of patients and how patient safety culture interventions can be improved to ensure translation of patient safety strategies at the frontlines of care. ORIGINALITY/VALUE: Greater acknowledgement of the patient and family experience provides organizations with an integral perspective to assist in defining and addressing deficiencies within their patient safety culture and to identify opportunities for improvement.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".