Surgical safety checklist compliance process as a moral hazard: An institutional ethnography
Bibliographic record
Abstract
BACKGROUND: Charting is an essential component of professional nursing practice and is arguably a key element of patient safety in surgery: without proper, objective, and timely documentation, both benign and tragical errors can occur. From surgery on wrong patients to wrong limbs, to the omission of antibiotics administration, many harms can happen in the operating room. Documentation has thus served as a safeguard for patient safety, professional responsibility, and professional accountability. In this context, we were puzzled by the practices we observed with respect to charting compliance with the surgical safety checklist (SSC) during a study of surgical teams in a large, urban teaching hospital in Canada (pseudonym 'C&C'). METHODS: This article leverages institutional ethnography and a subset of data from a larger study to describe and explain the social organisation of the system that monitored surgical safety compliance at C&C from the standpoint of operating room nurses. This data included fieldnotes from observations of 51 surgical cases, on-the-spot interviews with nurses, formal interviews with individuals who were involved in the design and implementation of the SSC, and open-ended questions from two rounds of survey of OR teams. FINDINGS: We found that the compliance form and not the SSC itself formed the basis for reporting. To meet hospital accuracy in charting goals and legislated compliance documentation reporting requirements nurses 'pre-charted' compliance with the surgical checklist. The adoption of this workaround technically violated nursing charting principles and put them in ethically untenable positions. CONCLUSIONS: Documenting compliance of the SSC constituted a moral hazard, constrained nurses' autonomy and moral agency, and obscured poor checklist adherence. The findings highlight how local and extra local texts, technologies and relations create ethical issues, raise questions about the effectiveness of resulting data for decision-making and contribute to ongoing conversations about nursing workarounds.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".