(Mis)Alignment in resident and advisor co‐regulated learning in competency‐based training
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: In implementing competence-based medical education (CBME), some Canadian residency programmes recruit clinicians to function as Academic Advisors (AAs). AAs are expected to help monitor residents' progress, coach them longitudinally, and serve as sources of co-regulated learning (Co-RL) to support their developing self-regulated learning (SRL) abilities. Implementing the AA role is optional, meaning each residency programme must decide whether and how to implement it, which could generate uncertainty and heterogeneity in how effectively AAs will "monitor and advise" residents. We sought to clarify how AA-resident dyads collaboratively interpret assessment data from multiple sources, co-create learning goals and action plans and attempt to enhance residents' SRL skills. METHODS: Shortly after each of their six meetings during two years of Internal Medicine residency, we conducted individual, brief interviews with AAs (N = 10) and residents (N = 10). We analysed transcripts using an abductive framework with theory-based and evidence-based sensitizing concepts. RESULTS: We collected 49 residents and 36 AA 'meeting debriefs', which produced rich data on how dyads variably engaged in SRL and Co-RL. Residents and AAs adopted "learning stances" that oriented their perceptions and approaches to Co-RL. Their stances did not always align within dyads. We found unique patterns in how stances evolved or devolved over time, and in how these changes impacted dyads' Co-RL processes. While some dyads evolved to engage in proactive co-regulation, most stayed consistent or oscillated reactively in their relationships, with little apparent Co-RL focused on helping residents to develop clinical competencies through SRL. We catalogued multiple influential sources of regulation of learning. CONCLUSION: The conceptually ideal form of Co-RL was not consistently achieved in this well-intended implementation of AA-resident dyads. To better translate 'coaching over time' from intention to practice, we recommend that residency programmes use Co-RL principles to refine CBME processes, including refining assessment tools, resident orientation sessions and faculty development practices.
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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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".