The ritualisation of the surgical safety checklist and its decoupling from patient safety goals
Bibliographic record
Abstract
Patient harm, patient safety and their governance have been ongoing concerns for policymakers, care providers and the public. In response to high rates of adverse events/medical errors, the World Health Organisation (WHO) advocated the use of surgical safety checklists (SSC) to improve safety in surgical care. Canadian health authorities subsequently made SSC use a mandatory organisational practice, with public reporting of safety indicators for compliance tied to pre-existing legislation and to reimbursements for surgical procedures. Perceived as the antidote for socio-technical issues in operating rooms (ORs), much of the SSC-related research has focused on assessing clinical and economic effectiveness, worker perceptions, attitudes and barriers to implementation. Suboptimal outcomes are attributed to implementations that ignored contexts. Using ethnographic data from a study of SSC at an urban teaching hospital (C&C), a critical lens and the concepts of ritual and ceremony, we examine how it is used, and theorise the nature and implications of that use. Two rituals, one improvised and one scripted, comprised C&C's SSC ceremony. Improvised performances produced dislocations that were ameliorated by scripted verification practices. This ceremony produced causally opaque links to patient safety goals and reproduced OR/medical culture. We discuss the theoretical contributions of the study and the implications for patient safety.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.059 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".