Stronger together:Empowering rural research through education-health partnerships
Bibliographic record
Abstract
Background Rural, remote and regional (R3) healthcare is shaped by unique settings, population characteristics and health conditions. Preparing the future R3 workforce for these contextual challenges requires an understanding of these unique attributes, something that is best achieved by R3-specific research. Research active health services are associated with better patient outcomes, but with limited numbers of experienced researchers in R3 areas, health services may seek to build research capacity. Research capacity building (RCB) is a process that adds to individual and organisational skills and abilities to conduct health research, shaping the future of rural and remote healthcare. Multiple approaches to RCB have been implemented and collaborations between educational institutions and health services is one example. These collaborations can harness respective strengths across organisations, with potential for high impact and mutual benefit. To understand these collaborations, this scoping review examines and describes the collaborative strategies employed to enhance the research capacity of health service staff. This abstract focuses on the R3 partnerships identified in the review whilst drawing on learnings from the wider review. Methods Informed by Arksey and O’Malley’s scoping review framework, we systematically searched four major databases: Medline, CINAHL, Embase, and Cochrane, focusing on publications after 1995. Collaboration, Research Capacity, Health Services, and Health Workforce were the primary concepts, contexts and populations guiding the search. These concepts were expanded using synonyms that were decided through team discussion. We established inclusion and exclusion criteria through iterative team discussions and used Covidence throughout the two-stage screening process and data extraction. Results From 1462 initially identified studies, 61 were selected for the review. Nineteen studies specifically focused on rural partnerships between educational institutions and health services for building research capacity of health service staff. Studies predominantly hailed from Australia, USA, UK, and Canada with rural models largely from Australia and Canada. Collaborative approaches to RCB included training, mentoring, shared funding, and networking. Rural partnerships focused on training health staff as first-time researchers, reflecting the emerging research skill set in R3 contexts and the need to build research from a widely distributed base. In rural areas funding partnerships were less prominent, reflecting both the low quanta of rural research funding and a tendency for rural research collaborations to rely on goodwill and existing relationships. Our findings emphasise the importance of tailoring approaches to local contexts, something that adds intrinsic value for both collaboration partners. Despite the known value of team-based healthcare, approaches focused largely on individual interventions like training and mentoring, with team-level interventions notably scarce. Conclusion Our review highlights that a diverse range of approaches have been implemented to develop research capacity through collaborations between health services and educational partners. For R3 partnerships, relationships are at the heart of collaborations. We recommend focussing on building from existing relationships and ensuring mutual goals are established early. Despite a resource-constrained environment, long-term collaborative success does rely on sustainable infrastructure and this must be a focus for partners to work towards. Working together can enable partnerships that prepare the R3 workforce for future research activity.
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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 | 0.179 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.018 | 0.035 |
| Open science | 0.003 | 0.036 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".