Education modalities for serious illness communication training: A scoping review on the impact on clinician behavior and patient outcomes
Bibliographic record
Abstract
BACKGROUND: Several clinician training interventions have been developed in the past decade to address serious illness communication. While numerous studies report on clinician attitudes and confidence, little is reported on individual education modalities and their impact on actual behavior change and patient outcomes. AIM: To examine what is known about the education modalities used in serious illness communication training and their impact on clinician behaviors and patient outcomes. DESIGN: A scoping review using the Joanna Briggs Methods Manual for Scoping Reviews was conducted to examine studies measuring clinician behaviors or patient outcomes. DATA SOURCES: Ovid MEDLINE and EMBASE databases were searched for English-language studies published between January 2011 and March 2023. RESULTS: = 5); though they were inconsistently structured. Studies reporting improved clinician skills tended to be in simulation settings with neither clinical practice nor patient outcomes explored. While some studies reported behavior changes or improved patient outcomes, they did not necessarily confirm improvements in clinician skills. As multiple modalities were commonly used and often embedded within quality improvement initiatives, the impact of individual modalities could not be determined. CONCLUSION: This scoping review of serious illness communication interventions found heterogeneity among education modalities used and limited evidence supporting their effectiveness in impacting patient-centered outcomes and long-term clinician skill acquisition. Well-defined educational modalities and consistent measures of behavior change and standard patient-centered outcomes are needed.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".