Learning From Patients: Why Continuity Matters
Bibliographic record
Abstract
PURPOSE: Patient continuity, described as the student participating in the provision of comprehensive care of patients over time, may offer particular opportunities for student learning. The aim of this study was to describe how students experience patient continuity and what they learn from it. METHOD: An interpretive phenomenological study was conducted between 2015 and 2016. Seventeen fourth-year medical students were interviewed following a longitudinal clinical placement and asked to describe their experiences of patient continuity and what they learned from each experience. Transcripts were analyzed by iteratively refining and testing codes, using health system definitions of patient continuity as sensitizing concepts to develop descriptive themes. RESULTS: Students described three different forms of patient continuity. Continuity of care, or relational continuity, enabled students to build trusting and professional relationships with their patients. Geographical continuity allowed students to access information about patients from electronic records and their preceptors which allowed students to achieve diagnostic closure and learn to reevaluate their decisions. Students valued the learning that accrued from following challenging patients and addressing challenging decisions over time. Although difficult, these patient continuity experiences led students to critical reflection that was both iterative and deep, leading to intentions for future behavior. CONCLUSIONS: Patient continuity in medical education does not depend solely on face-to-face continuity. Within various patient continuity experiences, following challenging patients and experiencing unanticipated diagnostic and management outcomes trigger critical reflection in students, leading to deep learning.
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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 | 0.017 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".