Systematic Scoping Review of Factors and Measures of Rurality: Toward the Development of a Rurality Index for Health Care Research in Japan
Bibliographic record
Abstract
Abstract BackgroundRural-urban health care disparities are an important topic. Hence, assessing rurality and evaluation of health outcomes based on rurality are indispensable. However, there is no universal measure of ruralityand Japan has no index to evaluate rurality for health care research. This study aimed to conduct a systematic scoping review to identify the important factors and methods of measuring rurality to inform the future development of a rurality index in Japan.MethodsFor our review, we searched six bibliographic databases (MEDLINE, PubMed, CINAHIL, ERIC, Web of Science and the Grey Literature Report) and official websitesof national governmentssuch asGovernment and Legislative Libraries Online Publications Portal (GALLOP),from 1 January 1989 to 31 December 2018. We extracted relevant variables used in the development of rurality indices, the formulas and reliability/validity measures.ResultsWe identified 17 rurality indices in 7 countries. These indices mainly aimed to assess access to health care or to decide incentives for health care providers. Frequently used variables for the indices were population size/density, travel distance/time to emergency care or referral centre. Many indices did not examine reliability and validity.ConclusionsWhile the concept of rurality and concerns about barriers to access to care for rural residents is shared by many countries, the operationalization of rurality is highly context-specific, with few universal measures or approaches to constructing a rurality index.The results will be helpful to develop a rurality index in Japan and other countries/areas.
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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 | 0.073 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.048 | 0.044 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".