Latent safety threat identification during in situ simulation debriefing: a qualitative analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it