Rural health research capacity building: an anchored solution
Bibliographic record
Abstract
Rural physicians face many challenges with providing rural health care, which often leads to innovative solutions. Despite their creativity with overcoming barriers, there is a lack of support for rural health research - an area of health care where research makes great impacts on small communities. Rural research capacity building (RRCB) is essential to support rural physicians so that they can conduct relevant research, but RRCB programs are sparse. Thus, our team at Memorial University of Newfoundland, Canada, has created an RRCB ecosystem through the 6for6 and Rural360 programs, which outline a pathway for rural physicians to make meaningful contributions to their communities through research. This article describes the RRCB ecosystem and explains how the 6for6 and Rural360 programs address the need for RRCB. Designed to train six rural physicians over six sessions per year, 6for6 fosters learning of research practices through a conceptual framework that envelops complexity science, systems thinking, and anchored instruction. The use of this framework allows the learning to be grounded in issues that are locally relevant for each participant and follows guiding principles that enable many types of learning. Rural360 continues the pathway by providing an in-house funding opportunity with an iterative review process that allows participants to continue developing their research skills and, ultimately, secure funding for their project. This anchored delivery model of RRCB programming is made possible through many support systems including staff, librarians, instructors, the university, and other stakeholders. It has successfully helped form communities of practice, promotes collaboration both between learners and with third parties, encourages self-organization with flexibility for learners outside of the in-house sessions, and ultimately drives social accountability in addressing local healthcare issues.
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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 | 0.051 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.008 | 0.052 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".