Walk a mile in my shoes: a chronic illness care workshop for first-year students.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A number of educators and recent medical school graduates have suggested a need to expand predoctoral training in chronic illness care. We developed a workshop to enhance first-year students' self-awareness regarding attitudes toward chronic illness care and to help them communicate effectively around patient self-care. METHODS: Students participated in a two-part workshop incorporating lectures, patient-centered interviewing role-plays, and an assignment requiring students to "have" a chronic illness and perform self-care tasks for 2 weeks. We assessed impact on chronic care knowledge by comparing pre- and post-workshop quiz scores. We also reviewed student evaluations of the experience. RESULTS: Of 96 students, 86 (90%) attended Session 1, and 91 (95%) attended Session 2. The mean (standard deviation) knowledge score improved from 6.4 (1.5) before the workshop to 8.4 (1.2) after the workshop (10 points possible). Of 53 students (55%) who completed an evaluation, most perceived the value of the workshop, including the self-care assignment and role-plays. Some felt more positively about chronic illness care following the workshop, and many indicated additional chronic care training in the clinical years would be welcome. CONCLUSIONS: An introductory workshop for first-year students led to increased knowledge of and improved attitudes toward chronic illness care. Longitudinal training in chronic illness care should be considered in predoctoral 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".