Faculty and resident perspectives on ambulatory care education: A collective case study of family medicine, psychiatry, and surgery
Bibliographic record
Abstract
BACKGROUND: Ambulatory care (AC) experiences within medical education are garnering increasing attention. We sought to understand how faculty and residents' describe their experiences of AC and ambulatory care education (ACEduc) within, between, and across disciplinary contexts. METHODS: We designed a Stakian collective case study, applying constructivist grounded theory analytic methods. Using purposive and snowball sampling, we interviewed 17 faculty and residents across three instrumental cases: family medicine, psychiatry, surgery. Through constant comparative analysis, we identified patterns within, between, and across cases. RESULTS: Family medicine and psychiatry saw AC as an inherent part of continuous, longitudinal care; surgery equated AC with episodic experiences in clinic, differentiating it from operating. Across cases, faculty and residents cautiously valued ACEduc, and in particular, considered it important to develop non-medical expert competencies (e.g., communication). However, surgery residents described AC and ACEduc as less interesting and a lower priority than operating. Educational structures mediated these views. CONCLUSION: Differences between cases highlight a need for further study, as universal assumptions about ACEduc's purposes and approaches may need to be tempered by situated, contextually-rich perspectives. How disciplinary culture, program structure, and systemic structure influence ACEduc warrant further consideration as does the educational potential for explicitly framing learners' perspectives.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".