The system, the resident, and the preceptor: a curricular approach to continuity of care training
Bibliographic record
Abstract
BACKGROUND: Continuity of care (CoC) is integral to the practice of comprehensive primary care, yet research in the area of CoC training in residency programs is limited. In light of distributed medical education and evolving accreditation standards, a rigorous understanding of the context and enablers contributing to CoC education must be considered in the design and delivery of residency training programs. APPROACH: At our preceptor-based community academic site, we developed a system-resident-preceptor (SRP) framework to explore factors that influence a resident's perception regarding CoC, and established variables in each area to enhance learning. We then implemented a two-year educational SRP intervention (SRPI) to one cohort of residents and their preceptors to integrate critical education factors and align teaching of continuity of care within curricular goals. EVALUATION: Evaluation of the intervention was based on resident interviews and faculty focus groups, and a qualitative phenomenological approach was used to analyze the data. While some factors identified are inherent to family medicine, the opportunity for reflection is a unique component to inculcate CoC learning. REFLECTION: The SRP innovation provides a unique framework to facilitate residents' understanding and development of CoC competency. Our model can be applied to all residency programs, including traditional academic sites as well as distributed training sites, to enhance CoC education.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".