Building rural health research capacity: protocol for a realist review
Bibliographic record
Abstract
INTRODUCTION: While individuals living in rural areas often have poorer health outcomes and reduced access to healthcare services compared with those in urban areas, there is a disproportionate gap in research examining rural health issues and identifying solutions to healthcare challenges. This is likely due to the numerous barriers to conducting rural health research, including the centralisation of research in urban areas and limited trained personnel and resources to conduct research in rural communities. This realist review aims to identify articles focused on building rural health research capacity and develop an evidence-based framework to be used by researchers, clinicians and policymakers to improve rural health services and well-being for rural populations. METHODS AND ANALYSIS: We will conduct a realist review using the following steps: (1) develop a search strategy, (2) conduct article screening and study selection, (3) perform data extraction, quality appraisal and synthesis, (4) engage stakeholders for feedback on our findings and (5) report our findings and engage in knowledge translation. Search terms include variations of the terms 'research', 'capacity building' and 'rural'. Databases include (since inception) Ovid MEDLINE, Embase, CINAHL Plus, APA PsycINFO, ERIC and Scopus. A separate search of the same databases was also designed to identify relevant theories or frameworks related to research capacity building, using variations of the terms 'research', "'capacity building', 'theory' and 'framework'. Studies will be screened by title and abstract and full text by two research team members and included based on their relevance to rural health research capacity building. We will exclude articles not published in English. We will also search the grey literature to identify rural health research centres, networks or training programmes that have not been described in the academic literature. Two research team members will extract relevant data from included studies and perform a qualitative analysis based on guidelines for realist reviews. ETHICS AND DISSEMINATION: This review does not require ethical approval as it draws on secondary data that is publicly available. The findings will be disseminated at academic conferences, published in peer-reviewed journals and summarised in a lay report for individuals interested in developing strategies, programmes or policies to improve rural health research. The results will inform individuals developing rural health research training programmes, establishing rural research centres, or others interested in building rural health research capacity. PROSPERO REGISTRATION NUMBER: CRD42023444072.
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 | 0.181 | 0.228 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.104 | 0.027 |
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".