Multiprofessional perspectives on the identification of latent safety threats via in situ simulation: a prospective cohort pilot study
Bibliographic record
Abstract
Objectives: To describe the association between participant profession and the number and type of latent safety threats (LSTs) identified during in situ simulation (ISS). Secondary objectives were to describe the association between both (a) participants' years of experience and LST identification and (b) type of scenario and number of identified LSTs. Methods: Emergency staff physicians (MDs), registered nurses (RNs) and respiratory therapists (RTs) participated in ISS sessions in the emergency department (ED) of a tertiary care teaching hospital. Adult and paediatric scenarios were designed to be high-acuity, low-occurrence resuscitation cases. Simulations were 10 min in duration. A written survey was administered to participants immediately postsimulation, collecting demographic data and perceived LSTs. Survey data was collated and LSTs were grouped using a previously described framework. Results: Thirteen simulation sessions were completed from July to November 2018, with 59 participants (12 MDs, 41 RNs, 6 RTs). Twenty-four unique LSTs were identified from survey data. RNs identified a median of 2 (IQR 1, 2.5) LSTs, significantly more than RTs (0.5 (IQR 0, 1.25), p=0.04). Within respective professions, MDs and RTs most commonly identified equipment issues, and RNs most commonly identified medication issues. Participants with ≤10 years of experience identified a median of 2 (IQR 1, 3) LSTs versus 1 (IQR 1, 2) LST in those with >10 years of experience (p=0.06). Adult and paediatric patient scenarios were associated with the identification of a median of 4 (IQR 3.0, 4.0) and 5 LSTs (IQR 3.5, 6.5), respectively (p=0.15). Conclusions: Inclusion of a multidisciplinary team is important during ISS in order to gain a breadth of perspectives for the identification of LSTs. In our study, participants with ≤10 years of experience and simulations with paediatric scenarios were associated with a higher number of identified LSTs; however, the difference was not statistically significant.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".