Psychological outcomes of debriefing healthcare providers who experience expected and unexpected patient death in clinical or simulation experiences: A scoping review
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To synthesise and map the literature on the psychological outcomes reported following debriefing of healthcare providers who experience expected and unexpected patient death in either clinical practice or simulation setting. BACKGROUND: Patient death occurs in both the clinical and simulation environments and can result in psychological stress in healthcare providers and students. While debriefing following patient death has demonstrated the ability to promote positive psychological outcomes, addressing the psychological or emotional stress of the event is inconsistently addressed. DESIGN: A scoping review was conducted using the Arksey and O'Malley framework. METHOD: The Cochrane Library, MEDLINE, CINAHL, PsycINFO, JBI and Scopus databases were searched with English language constraints and no limit on publication date. The Scoping Reviews (PRISMA-ScR) Checklist was used (Annals of Internal Medicine, 2018, 169, 467) (see Appendix S1). RESULTS: Eighteen articles (16 research papers and 2 review papers) met the inclusion criteria. Of the 16 research papers, 9 reported on debriefing models in the simulation environment and 7 in the clinical setting. The types of debriefing models found in the simulation setting tended to focus on healthcare providers' learning, while those in the clinical setting typically focused on healthcare providers' emotional reactions and resulted in positive psychological effects. CONCLUSION: Debriefing has the potential to positively affect psychological outcomes of healthcare providers who experience patient death. The type of debriefing that is selected is a key component to achieving these positive outcomes. RELEVANCE TO CLINICAL PRACTICE: This scoping review identified the debriefing frameworks used in both simulation and clinical environments following patient death events, and any associated psychological outcomes. There is a need for debriefing to occur after each death in either environment; however, there is a lack of evidence-based debriefing frameworks that can be used in both the clinical and simulation environments to promote positive psychological outcomes.
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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 | 0.032 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".