Advancing Educational Continuity in Primary Care Residencies
Bibliographic record
Abstract
Continuity of care is a core value of patients and primary care physicians, yet in graduate medical education (GME), creating effective clinical teaching environments that emphasize continuity poses challenges. In this Perspective, the authors review three dimensions of continuity for patient care-informational, longitudinal, and interpersonal-and propose analogous dimensions describing continuity for learning that address both residents learning from patient care and supervisors and interprofessional team members supporting residents' competency development. The authors review primary care GME reform efforts through the lens of continuity, including the growing body of evidence that highlights the importance of longitudinal continuity between learners and supervisors for making competency judgments. The authors consider the challenges that primary care residency programs face in the wake of practice transformation to patient-centered medical home models and make recommendations to maximize the opportunity that these practice models provide. First, educators, researchers, and policy makers must be more precise with terms describing various dimensions of continuity. Second, research should prioritize developing assessments that enable the study of the impact of interpersonal continuity on clinical outcomes for patients and learning outcomes for residents. Third, residency programs should establish program structures that provide informational and longitudinal continuity to enable the development of interpersonal continuity for care and learning. Fourth, these educational models and continuity assessments should extend to the level of the interprofessional team. Fifth, policy leaders should develop a meaningful recognition process that rewards academic practices for training the primary care workforce.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".