Challenges and Facilitators for Physicians and Medical Schools to Promote Social Accountability in Rural Communities: A Scoping Review and Thematic Analysis
Bibliographic record
Abstract
Abstract Background Social accountability aligns the health demands and priorities of the community and the commitment of medical schools. Existing literature has proposed contextually distinctive interventions encompassing geographical initiatives, acting networks or parties, and systematic categorizations for the implementation of social accountability in a rural setting. Research concerning social accountability implementation strategies has been insufficient. This study provides an overview of the existing literature surrounding the social accountability practice of physicians and medical schools, in rural settings, through evidence of the barriers and facilitators inherent to the rural communities. Methods This scoping review was conducted following the Arksey and O’Malley Framework. Web of Science, MEDLINE, EMBASE, SCOPUS, and CINAHL were searched for peer-reviewed studies published since 2000. Results A total of 2698 abstracts were identified, 180 full-text articles were reviewed, and 53 articles met the eligibility criteria across 15 different countries. Strategies used and problems inherent in promoting social accountability across physician practice and medical education are reported. Conclusions This scoping review synthesizes existing evidence on the barriers and facilitators of social accountability practices in rural settings globally. The identified literature captures recurring themes of medical infrastructure inadequacy, community immersion curriculum design, targeted admission, geographical isolation, and institutional or peer support.
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 | 0.049 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".