How not to waste a crisis: a qualitative study of problem definition and its consequences in three hospitals
Bibliographic record
Abstract
Objectives The prominence given to issues of patient safety in health care organizations varies, but little is known about how or why this variation occurs. We sought to compare and contrast how three English hospitals came to identify, prioritize and address patient safety issues, drawing on insights from the sociological and political science literature on the process of problem definition. Methods In-depth qualitative fieldwork, involving 99 interviews, 246 hours of ethnographic observation, and document collection, was carried out in three case-study hospitals as part of a wider mixed-methods study. Data analysis was based on the constant comparative method. Results How problems of patient safety came to be recognized, conceptualized, prioritized and matched to solutions varied across the three hospitals. In each organization, it took certain ‘triggers’ to problematize safety, with crises having a particularly important role. How problems were constructed – and whose definitions were prioritized in the process – was highly consequential for organizational response, influencing which solutions were seen as most appropriate, and allocation of responsibility for implementing them. Conclusions A process of problem definition is crucial to raising the profile of patient safety and to rendering problems amenable to intervention. How problems of patient safety are defined and constructed is highly consequential, influencing selection of solutions and their likely sustainability.
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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 | 0.030 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.023 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".