Using PEARLS to Guide Supportive Conversations With Nursing Students After Clinical Related Critical Incidents
Bibliographic record
Abstract
BACKGROUND: Undergraduate nursing students are at risk of exposure to clinical related critical incidents. The risk of exposure to critical incidents coupled with the potential for re-traumatization from past personal experiences adds complexity to teaching and learning in the clinical environment. METHOD: Providing clinical nurse educators with tools to facilitate timely and supportive conversations with students who have experienced clinical related critical incidents are imperative. The Promoting Excellence and Reflective Learning in Simulation (PEARLS) framework, with emphasis of a trauma-informed approach (TIA), may be used by clinical educators to guide supportive conversations with students who have experienced these unexpected events. RESULTS: Clinical educators' use of the PEARLS debriefing framework underpinned by a TIA creates opportunities for students to express their initial emotions, explore new insights, and navigate next steps. CONCLUSION: Supporting nursing students after a clinical related critical incident requires educators to be flexible and adaptable by offering individualized and personalized assistance to learners.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".