Attitudes and beliefs about the surgical safety checklist: Just another tick box?
Bibliographic record
Abstract
BACKGROUND: Following a landmark study showing decreased morbidity and mortality after implementation of the surgical safety checklist (SSC), it has been widely adopted into perioperative policy. We explored the impact of attitudes and beliefs surrounding the SSC on its uptake in Calgary. METHODS: We used qualitative methodology to examine factors influencing SSC use. We performed semistructured interviews based on Rogers' theory of diffusion of innovation. Purposive and snowball sampling were used to identify surgeons, anesthesiologists and operating room nurses from hospitals in Calgary. Data collection and analysis were based on grounded theory. Two individuals jointly analyzed data and achieved consensus on emerging themes. RESULTS: Generated themes included 1) the SSC has brought organization to previous informal perioperative checks, 2) the SSC is most helpful when it is simple, and 3) the 3 current components of the checklist are redundant. The briefing was considered the most important aspect and the debriefing the least important. Initially the SSC was difficult to implement owing to a shift in time management and perioperative culture; however, it has now assimilated into perioperative routine. Finally, though most participants agreed that the SSC might avoid some delays and complications, only a few believe there have been observable improvements to morbidity and mortality. CONCLUSION: Although the SSC has been integrated into perioperative practice in Calgary, participants believe that previous informal checkpoints were able to circumvent most perioperative issues. Although the SSC may help with flow and equipment, participants believe it fails to show a subjective, clinically important improvement.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".