A Comparison of Resident-Completed and Preceptor-Completed Formative Workplace-Based Assessments in a Competency-Based Medical Education Program
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In competency-based medical education (CBME), should resident self-assessments be included in the array of evidence upon which summative progress decisions are made? We examined the congruence between self-assessments and preceptor assessments of residents using assessment data collected in a 2-year Canadian family medicine residency program that uses programmatic assessment as part of their approach to CBME. METHODS: This was a retrospective observational cohort study using a learning analytics approach. The data source was archived formative workplace-based assessment forms (fieldnotes) stored in an online portfolio by family medicine residents and preceptors. Data came from three academic teaching sites over 3 academic years (2015-2016, 2016-2017, 2017-2018), and were analyzed in aggregate using nonparametric tests to evaluate differences in progress levels selected both within and between groups. RESULTS: In aggregate, first-year residents' self-reported progress was consistent with that indicated by preceptors. Progress level rating on fieldnotes improved over training in both groups. Second-year residents tended to assign themselves higher ratings on self-entered assessments compared with those assigned by preceptors; however, the effect sizes associated with these findings were small. CONCLUSIONS: Although we found differences in the progress level selected between preceptor-entered and resident-entered fieldnotes, small effect sizes suggest these differences may have little practical significance. Reasonable consistency between resident self-assessments and preceptor assessments suggests that benefits of guided self-assessment (eg, support of self-regulated learning, program efficacy monitoring) remain appealing despite potential risks.
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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 | 0.020 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".