Teaching social accountability by making the links: Qualitative evaluation of student experiences in a service-learning project
Bibliographic record
Abstract
BACKGROUND: Many medical students come into medicine with altruistic motives; few carry this altruism into their practice. As a result rural, remote and international areas are underserved by the medical community. Teaching social accountability may help students remain altruistic and encourage work in underserved areas. Making The Links (MTL) is a project designed to teach medical students the social aspects of medicine via service-learning. AIMS: The purpose of the study was to explore student reflections on their experiences during the MTL program. METHODS: Qualitative data analysis was conducted using structured open-ended written questionnaires. Fourteen students, representing three student cohorts, participated in the study. Data was collected between 2005 and 2007. RESULTS: Six themes emerged from qualitative data analysis. (1) relationships, (2) social determinants of health in real life, (3) community development, (4) interdisciplinarity, (5) linking health and communities, and (6) personal learning. Themes reflected the opportunities and challenges experienced by the students during the MTL project. Students reported that MTL was an essential component of their medical training. CONCLUSIONS: MTL is a promising model for using service-learning to teach social accountability in medical training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 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".