Continuity of supervision: Balancing continuous and episodic relationships for assessment and learning
Bibliographic record
Abstract
INTRODUCTION: Meaningful supervisor-resident relationships enhance feedback and learning, yet not all relationships reach this potential. While there is increasing interest in continuity of supervision (CoS) to build relationships that support feedback and promote learning, there remains a limited understanding of how relationships develop and influence assessment over time. The aim of this study was to explore how supervisors and learners in postgraduate medical education perceive CoS relationships and their impact on feedback and assessment. METHODS: We used constructivist grounded theory informed by the educational alliance to develop insight into how supervisor and resident perceptions of episodic and continuous relationships impact feedback and assessment. We interviewed 22 participants, including 14 family medicine residents and eight faculty advisors. We iteratively analysed the data concurrently with data collection. RESULTS: In episodic relationships, participants accepted superficiality for variety and diversity in feedback. In continuous relationships, we identified four sub-types. Our participants described how each of these relationships impacted their perceptions of the feedback and assessment information given or received and resulted in different steps taken in response to their perceptions: (i) Not developing-tolerate feedback and seek out additional assessors, (ii) deteriorating-avoid feedback and seek out alternative assessors, (iii) developing-value and tailor feedback and (iv) becoming a friendship-question bias in feedback and advocate for more assessors. CONCLUSIONS: Episodic and continuous relationships offered feedback and assessment value. However, deeper analysis of the continuous relationships revealed additional complexity. Understanding the nuances of CoS relationships is important for supporting successful relationships and improving feedback and assessment.
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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 | 0.018 | 0.043 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".