Continuity of supervision: Does it mean what we think it means?
Bibliographic record
Abstract
CONTEXT: Continuity of supervision (CoS) is generally accepted as an important element of competency-based medical education (CBME). However, collecting and interpreting evidence for its effectiveness are a challenge because we lack a shared understanding of CoS. Translating the available evidence about CoS into practice is an even greater challenge because the evidence largely exists in the undergraduate medical education (UME) literature, whereas literature about CBME is mostly situated in postgraduate medical education (PGME). PROPOSAL: We explore the potential dangers of basing assumptions of the importance of CoS in CBME on evidence from the UME level where CBME is yet to be widely implemented. First, we discuss current understandings of what is meant by CoS and examine some of its evidence and where such evidence comes from. Next, we consider relevant theories related to CoS in the context of CBME and review how it is conceptualised in different educational models. We then discuss some contextual and pedagogical differences between UME and PGME when CoS is considered. Finally, we propose a shared understanding of CoS and outline implications and next steps to determine if the benefits of CoS seen at the UME level will also manifest with PGME learners. CONCLUSIONS: We have the opportunity to undertake research to close our gap in knowledge about CoS at the PGME level using data emerging from our experiences with CBME. Selecting specific dimensions of CoS will allow research that is necessary to determine that what works at the UME level will also work at the PGME level as we continue to march towards CBME.
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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 | 0.035 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| 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".