Using Learning Analytics to Examine Differences in Assessment Forms From Continuous Versus Episodic Supervisors of Family Medicine Residents
Bibliographic record
Abstract
ABSTRACT Background It is assumed that there is a need for continuity of supervision within competency-based medical education, despite most evidence coming from the undergraduate medical education rather than the graduate medical education (GME) context. This evidence gap must be addressed to justify the time and effort needed to redesign GME programs to support continuity of supervision. Objective To examine differences in assessment behaviors of continuous supervisors (CS) versus episodic supervisors (ES), using completed formative assessment forms, FieldNotes, as a proxy. Methods The FieldNotes CS- and ES-entered for family medicine residents (N=186) across 3 outpatient teaching sites over 3 academic years (2015-2016, 2016-2017, 2017-2018) were examined using 2-sample proportion z-tests to determine differences on 3 FieldNote elements: competency (Sentinel Habit [SH]), Clinical Domain (CD), and Progress Level (PL). Results Sixty-nine percent (6104 of 8909) of total FieldNotes were analyzed. Higher proportions of CS-entered FieldNotes indicated SH3 (Managing patients with best practices), z=-3.631, P<.0001; CD2 (Care of adults), z=-8.659, P<.0001; CD3 (Care of the elderly), z=-4.592, P<.0001; and PL3 (Carry on, got it), z=-4.482, P<.0001. Higher proportions of ES-entered FieldNotes indicated SH7 (Communication skills), z=4.268, P<.0001; SH8 (Helping others learn), z=20.136, P<.0001; CD1 (Doctor-patient relationship/ethics), z=14.888, P<.0001; CD9 (Not applicable), z=7.180, P<.0001; and PL2 (In progress), z=5.117, P<.0001. Conclusions The type of supervisory relationship impacts assessment: there is variability in which competencies are paid attention to, which contexts or populations are included, and which progress levels are chosen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".