What works for whom in compassion training programs offered to practicing healthcare providers: a realist review
Bibliographic record
Abstract
BACKGROUND: Patients and families want their healthcare to be delivered by healthcare providers that are both competent and compassionate. While compassion training has begun to emerge in healthcare education, there may be factors that facilitate or inhibit the uptake and implementation of training into practice. This review identified the attributes that explain the successes and/or failures of compassion training programs offered to practicing healthcare providers. METHODS: Realist review methodology for knowledge synthesis was used to consider the contexts, mechanisms (resources and reasoning), and outcomes of compassion training for practicing healthcare providers to determine what works, for whom, and in what contexts. RESULTS: Two thousand nine hundred ninety-one articles underwent title and abstract screening, 53 articles underwent full text review, and data that contributed to the development of a program theory were extracted from 45 articles. Contexts included the clinical setting, healthcare provider characteristics, current state of the healthcare system, and personal factors relevant to individual healthcare providers. Mechanisms included workplace-based programs and participatory interventions that impacted teaching, learning, and the healthcare organization. Contexts were associated with certain mechanisms to effect change in learners' attitudes, knowledge, skills and behaviors and the clinical process. CONCLUSIONS: In conclusion this realist review determined that compassion training may engender compassionate healthcare practice if it becomes a key component of the infrastructure and vision of healthcare organizations, engages institutional participation, improves leadership at all levels, adopts a multimodal approach, and uses valid measures to assess 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 | 0.012 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".