A qualitative study of speaking out about patient safety concerns in intensive care units
Bibliographic record
Abstract
Much policy focus has been afforded to the role of "whistleblowers" in raising concerns about quality and safety of patient care in healthcare settings. However, most opportunities for personnel to identify and act on these concerns are likely to occur much further upstream, in the day-to-day mundane interactions of everyday work. Using qualitative data from over 900 h of ethnographic observation and 98 interviews across 19 English intensive care units (ICUs), we studied how personnel gave voice to concerns about patient safety or poor practice. We observed much low-level social control occurring as part of day-to-day functioning on the wards, with challenges and sanctions routinely used in an effort to prevent or address mistakes and norm violations. Pre-emptions were used to intervene when patients were at immediate risk, and included strategies such as gentle reminders, use of humour, and sharp words. Corrective interventions included education and evidence-based arguments, while sanctions that were applied when it appeared that a breach of safety had occurred included "quiet words", bantering, public exposure or humiliation, scoldings and brutal reprimands. These forms of social control generally functioned effectively to maintain safe practice. But they were not consistently effective, and sometimes risked reinforcing norms and idiosyncratic behaviours that were not necessarily aligned with goals of patient safety and high-quality healthcare. Further, making challenges across professional boundaries or hierarchies was sometimes problematic. Our findings suggest that an emphasis on formal reporting or communication training as the solution to giving voice to safety concerns is simplistic; a more sophisticated understanding of social control is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".