Considering supervision measures: a commentary on Li et al.’s (2024) scoping review
Bibliographic record
Abstract
This commentary on the Li et al. (2024) scoping review provides our perspectives on the strengths of this scoping review, challenges to scoping reviews in supervision research, future areas to build on this work, and suggests additional supervision measurement evaluation criteria. We hope our comments will spur further work building on this review to improve the feasibility and validity of supervision measures, to build an evidence-base for supervision, and crucially, to support supervisors and supervisees to deliver high-quality mental health care across the globe.
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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 | 0.165 | 0.588 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.040 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".