Using Longitudinal Milestones Data and Learning Analytics to Facilitate the Professional Development of Residents: Early Lessons From Three Specialties
Bibliographic record
Abstract
PURPOSE: To investigate the effectiveness of using national, longitudinal milestones data to provide formative assessments to identify residents at risk of not achieving recommended competency milestone goals by residency completion. The investigators hypothesized that specific, lower milestone ratings at earlier time points in residency would be predictive of not achieving recommended Level (L) 4 milestones by graduation. METHOD: In 2018, the investigators conducted a longitudinal cohort study of emergency medicine (EM), family medicine (FM), and internal medicine (IM) residents who completed their residency programs from 2015 to 2018. They calculated predictive values and odds ratios, adjusting for nesting within programs, for specific milestone rating thresholds at 6-month intervals for all subcompetencies within each specialty. They used final milestones ratings (May-June 2018) as the outcome variables, setting L4 as the ideal educational outcome. RESULTS: The investigators included 1,386 (98.9%) EM residents, 3,276 (98.0%) FM residents, and 7,399 (98.0%) IM residents in their analysis. The percentage of residents not reaching L4 by graduation ranged from 11% to 31% in EM, 16% to 53% in FM, and 5% to 15% in IM. Using a milestone rating of L2.5 or lower at the end of post-graduate year 2, the predictive probability of not attaining the L4 milestone graduation goal ranged from 32% to 56% in EM, 32% to 67% in FM, and 15% to 36% in IM. CONCLUSIONS: Longitudinal milestones ratings may provide educationally useful, predictive information to help individual residents address potential competency gaps, but the predictive power of the milestones ratings varies by specialty and subcompetency within these 3 adult care specialties.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".