Development and validation of a health profession education-focused scholarly mentorship assessment tool
Bibliographic record
Abstract
PROBLEM: PhD-trained researchers working in health professions education (HPE) regularly engage in one-on-one, or one-on-few, scholarly mentorship activities. While this work is often a formal expectation of these scientists' roles, rarely is there formal institutional acknowledgement of this mentorship. In fact, there are few official means through which a research scientist can document the frequency or quality of the scholarly mentorship they provide. APPROACH: OUTCOMES: The STHPE assessment tool has appropriate psychometric properties and evidence supporting acceptability. It can be used to document areas of strength and areas for improvement for research scientists engaged in HPE-related scholarly mentorship. NEXT STEPS: At present, the STHPE assessment tool is the only formally developed tool for which there is evidence of validity for use by PhD-trained researchers working in HPE to collect feedback on their scholarly mentorship skills. The STPHE has been used in promotion and tenure packages to document effectiveness and quality of scholarly mentorship.
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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 | 0.082 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".