Incorporating value-based healthcare projects in residency training: a mixed-methods study on the impact of participation on understanding and competency development
Bibliographic record
Abstract
OBJECTIVES: Stimulating the active participation of residents in projects with societally relevant healthcare themes, such as value-based healthcare (VBHC), can be a strategy to enhance competency development. Canadian Medical Education Directions for Specialists (CanMEDS) competencies such as leader and scholar are important skills for all doctors. In this study, we hypothesise that when residents conduct a VBHC project, CanMEDS competencies are developed. There is the added value of gaining knowledge about VBHC. DESIGN: An explorative mixed-methods study assessing residents' self-perceived learning effects of conducting VBHC projects according to three main components: (1) CanMEDS competency development, (2) recognition of VBHC dilemmas in clinical practice, and (3) potential facilitators for and barriers to implementing a VBHC project. We triangulated data resulting from qualitative analyses of: (a) text-based summaries of VBHC projects by residents and (b) semistructured interviews with residents who conducted these projects. SETTING: Academic and non-academic hospitals in the Netherlands. PARTICIPANTS: Out of 63 text-based summaries from residents, 56 were selected; and out of 19 eligible residents, 11 were selected for semistructured interviews and were included in the final analysis. RESULTS: Regarding CanMEDS competency development, the competencies 'leader', 'communicator' and 'collaborator' scored the highest. Opportunities to recognise VBHC dilemmas in practice were mainly stimulated by analysing healthcare practices from different perspectives, and by learning how to define costs and relate them to outcomes. Finally, implementation of VBHC projects is facilitated by a thorough investigation of a VBHC dilemma combined with an in-depth stakeholder analysis. CONCLUSION: In medical residency training programmes, competency development through active participation in projects with societally relevant healthcare themes-such as VBHC-was found to be a promising strategy. From a resident's perspective, combining a thorough investigation of the VBHC dilemma with an in-depth stakeholder analysis is key to the successful implementation of a VBHC project.
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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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".