Latent safety threat identification during in situ simulation debriefing: a qualitative analysis
Bibliographic record
Abstract
Background: Latent safety threats (LSTs) in healthcare are hazards or conditions that risk patient safety but are not readily apparent without system stress. In situ simulation (ISS), followed by post-scenario debriefing is a common method to identify LSTs within the clinical environment. The function of post-ISS debriefing for LST identification is not well understood. Objectives: This study aims to qualitatively characterise the types of LSTs identified during ISS debriefing. Methods: We conducted 12 ISS trauma scenarios followed by debriefing at a Canadian, Level 1 trauma centre. We designed the scenarios and debriefing for 15 and 20 min, respectively. Debriefings focused on LST identification, and each session was audio recorded and transcribed. We used an inductive approach with qualitative content analysis to code text data into an initial coding tree. We generated refined topics from the coded text data. Results: We identified five major topics: (1) communication and teamwork challenges, (2) system-level issues, (3) resource constraints, (4) positive team performance and (5) potential improvements to the current systems and processes. Conclusions: During simulation debriefing sessions for LST identification, participants discussed threats related to communication and interpersonal issues. Safety issues relating to equipment, processes and the physical space received less emphasis. These findings may guide health system leaders and simulation experts better understanding of the strengths and limitations of simulation debriefing for LST identification. Further studies are required to compare ISS-based LST identification techniques.
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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 | 0.049 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".