CheckPOINT: a simple tool to measure Surgical Safety Checklist implementation fidelity
Bibliographic record
Abstract
INTRODUCTION: The WHO Surgical Safety Checklist (SSC) is a communication tool that improves teamwork and patient outcomes. SSC effectiveness is dependent on implementation fidelity. Administrative audits fail to capture most aspects of SSC implementation fidelity (ie, team communication and engagement). Existing research tools assess behaviours during checklist performance, but were not designed for routine quality assurance and improvement. We aimed to create a simple tool to assess SSC implementation fidelity, and to test its reliability using video simulations, and usability in clinical practice. METHODS: The Checklist Performance Observation for Improvement (CheckPOINT) tool underwent two rounds of face validity testing with surgical safety experts, clinicians and quality improvement specialists. Four categories were developed: checklist adherence, communication effectiveness, attitude and engagement. We created a 90 min training programme, and four trained raters independently scored 37 video simulations using the tool. We calculated intraclass correlation coefficients (ICC) to assess inter-rater reliability (ICC>0.75 indicating excellent reliability). We then trained two observers, who tested the tool in the operating room. We interviewed the observers to determine tool usability. RESULTS: The CheckPOINT tool had excellent inter-rater reliability across SSC phases. The ICC was 0.83 (95% CI 0.67 to 0.98) for the sign-in, 0.77 (95% CI 0.63 to 0.92) for the time-out and 0.79 (95% CI 0.59 to 0.99) for the sign-out. During field testing, observers reported CheckPOINT was easy to use. In 98 operating room observations, the total median (IQR) score was 25 (23-28), checklist adherence was 7 (6-7), communication effectiveness was 6 (6-7), attitude was 6 (6-7) and engagement was 6 (5-7). CONCLUSIONS: CheckPOINT is a simple and reliable tool to assess SSC implementation fidelity and identify areas of focus for improvement efforts. Although CheckPOINT would benefit from further testing, it offers a low-resource alternative to existing research tools and captures elements of adherence and team behaviours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".