Patient involvement in resident assessment within the Competence by Design context: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Patients can contribute to resident assessment in Competence by Design (CBD). This study explored the extent, nature, as well as the facilitators and hindrances of patient involvement in resident assessment within and across Canadian specialty/sub-specialty/special programs that are transitioning or have transitioned to CBD. METHODS: We used a two-phase sequential explanatory mixed-methods design. In Phase 1, we surveyed program directors (PDs). In Phase 2, we interviewed PDs from Phase 1. RESULTS: In Phase 1, 63 (62.4%) respondents in the CBD preparation stage, do not know if patients will be involved in resident assessment, 21 (20.8%) will involve patients, and 17 (16.8%) will not involve patients. Of those in the field-testing or implementation stages, 24 (72.7%) do not involve patients in resident assessment, five (15.2%) do involve patients, and four (12.1%) do not know if they involve patients. In Phase 2, 12 interviewees raised nine factors that facilitate or hinder patient involvement including, patients' interests/abilities, guidelines/processes for patient involvement, type of Entrustable Professional Activities, type of patient interactions in programs, and support from healthcare organizations. CONCLUSION: Patient involvement in resident assessment is limited. We need to engage in discussions on how to support such involvement within CBD.
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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 | 0.061 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".