‘I think we should just listen and get out’: a qualitative exploration of views and experiences of Patient Safety Walkrounds
Bibliographic record
Abstract
OBJECTIVE: This article is an exploration of views and experiences of Patient Safety Walkrounds, a widely recommended strategy for identifying patient safety problems and improving safety culture. DESIGN AND SETTING: Qualitative analysis of semistructured, in-depth interviews with 11 senior leaders and 33 front-line staff at two major teaching hospitals with mature walkrounds programmes, collected as part of a larger mixed-methods evaluation. RESULTS: Despite differences in the structure of the two walkrounds programmes, senior leaders at both institutions reported attitudes and behaviours that contradict the stated goals and principles of walkrounds. Senior leaders tended to regard executive visibility as an end in itself and generally did not engage with staff concerns beyond the walkrounds encounter. Some senior leaders believed they understood patient safety issues better than front-line staff and even characterised staff concerns as 'stupid'. Senior leaders acknowledged that they often controlled the conversations, delimiting what counted as patient safety problems and sometimes even steered the conversations to predetermined topics. Some front-line staff made note of these contradictions in their interviews. DISCUSSION/CONCLUSIONS: Our study found that walkrounds may inadvertently lead to counter-productive attitudes by senior leaders at odds with the recommended principles of walkrounds. The results demonstrate similar attitudes from senior leaders at two hospitals with quite different formats for walkrounds, suggesting that this pattern may exist elsewhere. Better preparation of senior leaders prior to the walkrounds may help to avoid the counter-productive attitudes and dynamics that we identified.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 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".