It takes a community to train a future physician: social support experienced by medical students during a community-engaged longitudinal integrated clerkship
Bibliographic record
Abstract
Background:Social support may be beneficial for medical students who must develop adaptive strategies to respond to the demands and challenges during third-year clerkship.We provide a detailed description of the supportive behaviours experienced by third-year students during a longitudinal integrated clerkship (LIC) in the context of rural family medicine. Methods:Informed by a social constructivist research paradigm, we undertook a qualitative study to understand from the students’ perspectives the presence and characteristics of social support available during a LIC.Data were collected from conversational interviews at three points during the eight-month clerkship year, pre-, during, and post-clerkship, to explore how 12 medical students experienced social support. We employed an innovative methodological approach, the guided walk method, to gain the students’ stories in the contexts where they were taking place. Results: The participants described the relationships they developed with various sources of social support such as (a) preceptors, (b) peers, (c) family, (d) health professionals, and (e) community members. Conclusion:Various individuals representing communities of practice such as the medical profession and community members were intimately related to the longitudinal aspects of the students’ experiences. The findings lend credence to the view that it really does take a community to train a future physician.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".