Facilitating Learner-Centered Transition to Residency: A Scoping Review of Programs Aimed at Intrinsic Competencies
Bibliographic record
Abstract
Phenomenon: There is currently a move to provide residency programs with accurate competency-based assessments of their candidates, yet there is a gap in knowledge regarding the role and effectiveness of interventions in easing the transition to residency. The impact of key stakeholder engagement, learner-centeredness, intrinsic competencies, and assessment on the efficacy of this process has not been examined. The objective of this scoping review was to explore the nature of the existing scholarship on programs that aim to facilitate the transition from medical school to residency. Approach: We searched MEDLINE and EMBASE from inception to April 2020. Programs were included if they were aimed at medical students completing undergraduate medical training or first year residents and an evaluative component. Two authors independently screened all abstracts and full text articles in duplicate. Data were extracted and categorized by type of program, study design, learner-centeredness, key stakeholder engagement, the extent of information sharing about the learner to facilitate the transition to residency, and specific program elements including participants, and program outcomes. We also extracted data on intrinsic (non-Medical Expert) competencies, as defined by the CanMEDS competency framework. Findings: Of the 1,006 studies identified, 55 met the criteria for inclusion in this review. The majority of the articles that were eligible for inclusion were from the United States (n = 31, 57%). Most of the studies (n = 47, 85%) employed quantitative, or mixed method research designs. Positive outcomes that were commonly reported included increased self-confidence, competence in being prepared for residency, and satisfaction with the transition program. While a variety of learner-centered programs that focus on specific intrinsic competencies have been implemented, many (n = 29, 52%) did not report engaging learners as key stakeholders in program development. Insights: While programs that aim to ease the transition from medical school to residency can enhance both Medical Expert and other intrinsic competencies, there is much room for novel transition programs to define their goals more broadly and to incorporate multiple areas of professional development. The existing literature highlights various gaps in approaches to easing the transition from medical school to residency, particularly with respect to key stakeholder engagement, addressing intrinsic CanMEDS competencies, and focusing on individual learners’ needs.
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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 | 0.025 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".