International declaration on rural mental health research: 10 guiding principles and standards
Bibliographic record
Abstract
Rural communities have unique mental health needs and challenges which are often related to the uniqueness of the community itself. On a per-capita basis, the investment in rural mental health research is far less than that in urban communities. Added to this, rural communities are often at risk of researchers, based in large urban universities, visiting, conducting the research with minimal engagement with local stakeholders and limited understanding of the community's social-service-environmental context. Often this research leaves no visible benefit to the community with respect to increased knowledge, resources or community capacity. This commentary is based on the insights of a panel of authors from 9 countries, each with extensive experience of rural mental health research and work. And it seeks to stimulate the discourse on responsible rural mental health practice. The aim of this commentary is to provide a reference on research practice for novice and experienced researchers on rural mental health research and practice, to assist policymakers, government and funding bodies to establish appropriate standards and guidelines for rural mental health research, and support rural communities to advocate for equity of funding and sustainable research as they engage with researchers, funders and governments. The 10 standards in this declaration will help guide researchers toward research that is beneficial to rural communities and also help develop the local community's research capability, which ultimately will serve to enhance the mental health and well-being of rural communities.
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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 | 0.061 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.046 | 0.068 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".