Longitudinal Reliability of Milestones-Based Learning Trajectories in Family Medicine Residents
Bibliographic record
Abstract
Importance: Longitudinal Milestones data reported to the Accreditation Council for Graduate Medical Education (ACGME) can be used to measure the developmental and educational progression of learners. Learning trajectories illustrate the pattern and rate at which learners acquire competencies toward unsupervised practice. Objective: To investigate the reliability of learning trajectories and patterns of learning progression that can support meaningful intervention and remediation for residents. Design, Setting, and Participants: This national retrospective cohort study included Milestones data from residents in family medicine, representing 6 semi-annual reporting periods from July 2016 to June 2019. Interventions: Longitudinal formative assessment using the Milestones assessment system reported to the ACGME. Main Outcomes and Measures: To estimate longitudinal consistency, growth rate reliability (GRR) and growth curve reliability (GCR) for 22 subcompetencies in the ACGME family medicine Milestones were used, incorporating clustering effects at the program level. Latent class growth curve models were used to examine longitudinal learning trajectories. Results: This study included Milestones ratings from 3872 residents in 514 programs. The Milestones reporting system reliably differentiated individual longitudinal patterns for formative purposes (mean [SD] GRR, 0.63 [0.03]); there was also evidence of precision for model-based rates of change (mean [SD] GCR, 0.91 [0.02]). Milestones ratings increased significantly across training years and reporting periods (mean [SD] of 0.55 [0.04] Milestones units per reporting period; P < .001); patterns of developmental progress varied by subcompetency. There were 3 or 4 distinct patterns of learning trajectories for each of the 22 subcompetencies. For example, for the professionalism subcompetency, residents were classified to 4 groups of learning trajectories; during the 3-year family medicine training period, trajectories diverged further after postgraduate year (PGY) 1, indicating a potential remediation point between the end of PGY 1 and the beginning of PGY 2 for struggling learners, who represented 16% of learners (620 residents). Similar inferences for learning trajectories were found for practice-based learning and improvement, systems-based practice, and interpersonal and communication skills. Subcompetencies in medical knowledge and patient care demonstrated more consistent patterns of upward growth. Conclusions and Relevance: These findings suggest that the Milestones reporting system provides reliable longitudinal data for individualized tracking of progress in all subcompetencies. Learning trajectories with supporting reliability evidence could be used to understand residents' developmental progress and tailored for individualized learning plans and remediation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".