Understanding how and under what circumstances decision coaching works for people making healthcare decisions: a realist review
Bibliographic record
Abstract
BACKGROUND: Decision coaching is non-directive support delivered by a trained healthcare provider to help people prepare to actively participate in making healthcare decisions. This study aimed to understand how and under what circumstances decision coaching works for people making healthcare decisions. METHODS: We followed the realist review methodology for this study. This study was built on a Cochrane systematic review of the effectiveness of decision coaching interventions for people facing healthcare decisions. It involved six iterative steps: (1) develop the initial program theory; (2) search for evidence; (3) select, appraise, and prioritize studies; (4) extract and organize data; (5) synthesize evidence; and (6) consult stakeholders and draw conclusions. RESULTS: We developed an initial program theory based on decision coaching theories and stakeholder feedback. Of the 2594 citations screened, we prioritized 27 papers for synthesis based on their relevance rating. To refine the program theory, we identified 12 context-mechanism-outcome (CMO) configurations. Essential mechanisms for decision coaching to be initiated include decision coaches', patients', and clinicians' commitments to patients' involvement in decision making and decision coaches' knowledge and skills (four CMOs). CMOs during decision coaching are related to the patient (i.e., willing to confide, perceiving their decisional needs are recognized, acquiring knowledge, feeling supported), and the patient-decision coach interaction (i.e., exchanging information, sharing a common understanding of patient's values) (five CMOs). After decision coaching, the patient's progress in making or implementing a values-based preferred decision can be facilitated by the decision coach's advocacy for the patient, and the patient's deliberation upon options (two CMOs). Leadership support enables decision coaches to have access to essential resources to fulfill their role (one CMOs). DISCUSSION: In the refined program theory, decision coaching works when there is strong leadership support and commitment from decision coaches, clinicians, and patients. Decision coaches need to be capable in coaching, encourage patients' participation, build a trusting relationship with patients, and act as a liaison between patients and clinicians to facilitate patients' progress in making or implementing an informed values-based preferred option. More empirical studies, especially qualitative and process evaluation studies, are needed to further refine the program theory.
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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 | 0.028 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".