Characterizing and Contextualizing the Use of the Surgical Safety Checklist in General Surgery
Bibliographic record
Abstract
The Surgical Safety Checklist (SSCL) is a widely implemented intervention; however, limited studies have explored system factors that impact adherence to proper checklist completion. Using an audio-visual recording technology called the Operating Room Blackbox (ORBB), the goal of this study was to characterize the level of checklist completion and identify work system factors that impact the use of the SSCL in general laparoscopic surgery. Thirty-six cases captured by the ORBB in a hospital in Toronto, Ontario were reviewed using an SSCL audit tool to collect data on item-level adherence. The Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model was applied to the observation notes from the ORBB recordings to identify work system factors influencing checklist use. On average, across all 36 cases, 8 of the 29 checklist items were completed (30.1%), with debriefing being completed most frequently of the 3 checklist sections. Of the 29 checklist items, commonly completed items were: patient concerns addressed, surgical counts complete, and procedure name, while items in the timeout section were completed the least. Notably, factors related to person (e.g., confirmation of patient information amongst surgical team) and tools and technology (e.g., use of checklist in combination with patient chart) were identified as facilitators to checklist use, while factors relating to tasks (e.g., redundancy of checklist items with existing workflow), tools and technology (e.g., some checklist items not applicable to some procedures), organization (e.g, timing of checklist items and absence of team member), and internal environment (e.g., music volume in the OR) were identified as potential barriers to checklist use. By understanding how system factors contribute to checklist use and item-level adherence, we can identify ways to improve the checklist to meet the needs of the OR team and enhance integration of the SSCL into existing workflows.
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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 | 0.016 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".