The development of the Canadian rural health research society: Creating capacity through connections
Bibliographic record
Abstract
Context: The organization of rural health research in Canada has been a recent development. Over the past 8 years, rural and remote researchers from more than 15 universities and agencies across Canada have engaged in a process of research capacity building through the development of a network, the Canadian Rural Health Research Society (CRHRS) among the scientifically and geographically diverse researchers and their community partners. The purpose of this article is to discuss the development of the CRHRS as well as the challenges and lessons learned about creating networks and building capacity among rural and remote health researchers. \nIssue: Key elements of network development have included identifying and developing multidisciplinary research groupings, maintaining ongoing connections among researchers, and promoting the sharing of expertise and resources for research training. The focus has been on supporting research excellence among networks of researchers in smaller centres. Activities include a national annual scientific meeting, the informal formation of several regional and national research networks in specific areas, and the development of training opportunities. Challenges have included the issues of sustaining communication, addressing a range of networking and capacity-enhancement needs, cooperating in an environment that rewards competition, obtaining resources to support a secretariat and research activities, and balancing the demands to foster research excellence with the needs to create infrastructure and advocate for adequate research funding. \nLessons learned: The CRHRS has learned how to begin to support researchers with diverse interests and needs across sectors and wide geographical areas, specifically by: (1) focusing on research development through creating and supporting trusting connections among researchers; (2) building the science first, followed by infrastructure development; (3) making individual researchers the nodes in the network; (4) being inclusive by accommodating a wide variety of researchers and researcher strengths; (5) emphasizing social exchange, knowledge exchange, and mentoring in annual scientific meetings; (6) taking opportunities to develop separate projects while finding ways to link them; (7) finding a balance between advancing the science and advocating for adequate funding and appropriate peer review; (8) developing a network organizational structure that is both stable and flexible; and (9) maintaining sustained visionary leadership.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.026 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.005 |
| 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".