Longitudinal Reliability of Milestones-Based Learning Trajectories in Family Medicine Residents
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,073 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».