The experiences of medical and pharmacy students participating in a collaborative online international learning on social accountability and global health: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Social accountability aims to promote a collective ethic that upholds the fundamental values of equity, efficiency, solidarity, and social justice in healthcare and is now considered as a critical mission of academic health centers. Collaborative Online International Learning (COIL) is a pedagogical approach that uses digital technology to provide experiential international learning, specifically for increasingly diverse and multicultural healthcare work environments. The SOLID'AIRS program is an innovative French-language COIL that aims to set up international exchanges and workshops on social accountability between health sciences students. The aim of our study was to assess the impact of participation in the SOLID'AIRS program on medical and pharmacy students. METHOD: Five universities in four different countries were involved in SOLID'AIRS. We conducted a qualitative study by performing individual, semi-directed interviews with students who participated in the program from 2021 to 2022. A thematic analysis was conducted in five chronological phases: (1) reading, (2) descriptive coding, (3) conceptual coding, (4) identification of themes and (5) production of a coherent thematic structure. RESULTS: After including sixteen student participants, 13 medical and 3 pharmacy students, we identified four main themes related to the impact of participation: (1) previous experiences in social accountability and international learning, (2) perception of the program, (3) perceived impacts of the program, and (4) difficulties encountered and avenues to improve the program. Overall, the program was well received by all participants who reported the advantages and limitations of the online learning format. The primary advantage of this format was its feasibility. The participants noted both professional and personal benefits of the program for their current and future practice, including greater reflexivity towards health sciences practice. Based on the challenges faced during the program, particularly in coordinating group work and communication, participants suggested increased supervision of group projects by collaborators, and organizing at least one in-person meeting for future editions. The participants reported encountering difficulties during the COIL and suggested ways of improvement. CONCLUSION: Participating in a COIL on social accountability appears to be an effective way to adopt a reflective approach to medical practice and should be implemented and evaluated in other educational contexts.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".