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
BACKGROUND: Rural-urban health care disparities are an important topic in health services research. Hence, developing valid and reliable tools to measure rurality is needed to support high quality research. However, Japan, has no index to measure rurality for health care research. In this study, we conducted a systematic scoping review to identify the important factors and methodological approaches to consider in a rurality index to inform the development of a rurality index for Japan. METHODS: For our review, we searched six bibliographic databases (MEDLINE, PubMed, CINAHIL, ERIC, Web of Science and the Grey Literature Report) and official websites of national governments such as Government 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 used to calculate indices, and any measures for reliability and validity of these indices. RESULTS: We identified 17 rurality indices from 7 countries. These indices were primarily developed to assess access to health care or to determine eligibility for incentives for health care providers. Frequently used factors in these indices included population size/density and travel distance/time to emergency care or referral centre. Many indices did not report reliability or validity measures. CONCLUSIONS: While 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 in the development of a rurality index in Japan and in other countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".