The Limerick Declaration on Rural Health Care 2022
Bibliographic record
Abstract
The 19th World Rural Health Conference, hosted in rural Ireland and the University of Limerick, with over 650 participants coming from 40 countries and an additional 1600 engaging online, has carefully considered how best rural communities can be empowered to improve their own health and the health of those around them. The conference also considered the role of national health systems and all stakeholders, in keeping with the commitments made through the Sustainable Development Goals and the enjoyment of the highest attainable standard of health as one of the fundamental rights of every human being. This conference declaration, the Limerick Declaration on Rural Healthcare, is designed to inform rural communities, academics and policymakers about how to achieve the goal of delivering high quality health care in rural and remote areas most effectively, with a particular focus on the Irish healthcare system. Congruent with current evidence and best international practice, the participants of the conference endorsed a series of recommendations for the creation of high quality, sustainable and cost-effective healthcare delivery for rural communities in Ireland and globally. The recommendations focused on four major themes: rural healthcare needs and delivery, rural workforce, advocacy and policy, and research for rural health care. Equal access to health care is a crucial marker of democracy. Hence, we call on all governments, policymakers, academic institutions and communities globally to commit to providing their rural dwellers with equitable access to health care that is properly resourced and fundamentally patient-centred in its design.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.024 | 0.018 |
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".