Rural physicians and social capital: the potential and promises of a rural health research capacity building program
Bibliographic record
Abstract
BACKGROUND: Accessible and contextually relevant healthcare research programs and networks for rural physicians are exceedingly rare, which hinders the development of social capital in an already isolating profession. This study aims to examine the impact of the Rural Health Research Capacity Building (RRCB) Program on enhancing cognitive, structural, and relational social capital through comprehensive research skills training, supported by professional teams and resources. METHODS: This study uses a mixed-methods approach with utilization of qualitative and quantitative data and pre-post quasi-experimental design. Data were collected prior and after completion of the program by means of surveys, focus group, and observation. Thirty-five rural physicians participated in this study from 2014 to 2021. RESULTS: The results show a significant increase in cognitive (pre-program = 0.37 vs. post-program = 0.61, p < .001), structural (pre-program = 0.58 vs. post-program = 0.81, p < .001), and relational (pre-program = 0.49 vs. post-program 0.69, p < .001) components of social capital. Focus group discussions and observation data supported these findings, particularly highlighting that research capacity-building programs tailored to the needs of rural physicians can enhance collective values, improve the quality of relationships, and foster communities of research-focused practice. CONCLUSIONS: Being equipped with a shared system of meanings and interpretations, research knowledge and resources, and a professional research network appears to play a critical role in enhancing social capital in rural health research. The RRCB program effectively improves social capital among rural and remote physicians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".