Rural and urban disparities in access and quality of healthcare in the Japanese healthcare system: a scoping review
Bibliographic record
Abstract
BACKGROUND: The rural-urban disparity in healthcare quality is a global issue. Compared with living in urban areas, living in rural areas is associated with poorer healthcare outcomes. Moreover, the shortage of healthcare providers in rural areas is a worldwide concern. This scoping review aims to map existing evidence regarding rural-urban disparities in access and quality of healthcare in Japan using the Donabedian model as a theoretical framework and to identify conceptual and measurement gaps. METHODS: This review targeted published articles and gray literature. We included documents that (1) were based on Japanese populations and (2) compared the quality of care between defined rural and urban areas. We excluded articles if they (1) were published during or before 2005 since the Japanese government amended the Medical Care Law in 2006; (2) focused exclusively on urban or rural areas; or (3) were not published in English or Japanese. This study employed PubMed, EMBASE, Web of Science, the Japanese medical literature database, ICHUSHI, and CiNii Research. We extracted quality indicators (structure, process, and outcomes) based on the Donabedian model. We recorded the definitions or indicators of rurality described by the studies. RESULTS: Out of 5,020 articles, 15 were included. Only one study was conducted in a primary care setting. Moreover, no study evaluated the "outcomes" of the Donabedian model in a primary care setting. Regarding the definitions or indices of rurality, the most commonly used indicator of rurality was population size, followed by population density. The cutoff values or descriptions of rurality using these indicators differed across studies. CONCLUSION: This study mapped rural-urban disparities in access and quality of healthcare in Japan. These findings highlight the need to evaluate rural-urban disparities in the "outcomes" of care in primary care settings in Japan and the lack of common indicators of rurality.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".