Service Learning for Medical Students: Program Development and Students' Reflections.
Bibliographic record
Abstract
We designed a cross-disciplinary interdepartmental volunteer program, which involved student participation in care teams for the elderly living alone. Our aim was to enhance communication between students and the elderly. Students were expected to meet and learn to get along with the elderly, to develop listening and communication skills, and learn to cooperate with student participants in other services. Students were required to devote at least 14 hours per semester to this two-semester program. Between September 2008 and June 2009, 19 students (1 st semester), 34 students (2 nd semester), 7 students (both 1 st and 2 nd semesters), respectively, and 15 elderly participants became involved in the program. Students were divided into 15 groups (each with 2–4 students), and each group visited the assigned elderly person at least 6 times per semester. According to student accounts, these visits improved their interpersonal and communication skills and their ability to express concerns with self-confidence. Our analysis of students’ reflections found that early exposure to such community experiences increases their capacity for self-reflection and teaches them how to show respect. The opportunity to develop empathic communication skills with the elderly and learn to cooperate with faculty and colleagues can be beneficial to students in their future medical practice and strengthen the quality of community care.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".