Imbalances in rural primary care: brief based on a scoping literature review with an emphasis on the WHO European Region.Technical series on primary health care.
Bibliographic record
Abstract
Method and scope of this brief\nThis brief and its background report are based on a scoping literature review aiming to rapidly provide a summary of best practices and approaches to solve imbalances in rural primary care. The review covered research dealing with primary care in rural and remote areas that has been published between 2008 – the year that the landmark World health report – Primary care: now more than ever was launched – and the summer of 2018. Where available, in review studies, we took into account the methodological quality of studies. However, a methodological assessment of individual studies has not been undertaken. This review also offers a situation description regarding the problems of access to primary care in rural areas\nand describes what is known about its root causes and consequences. The main focus of this brief and its background report is on the WHO European Region, but a substantial portion of the included studies comes from several large countries outside the WHO European Region, such as Australia, Canada and the United States, that have been coping with this problem for a long time. Because of the global nature of available evidence, findings and reported policy options are globally applicable.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".