Rural definition of health: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: The advent of patient-centered care challenges policy makers, health care administrators, clinicians, and patient advocates to understand the factors that contribute to effective patient activation. Improved understanding of how patients think about and define their health is needed to more effectively "activate" patients, and to nurture and support patients' efforts to improve their health. Researchers have intimated for over 25 years that rural populations approach health in a distinct fashion that may differ from their non-rural counterparts. METHODS: We conducted a systematic review of the literature to assess the extent and strength of evidence for rural definition of health. Studies were eligible for inclusion if they were published in English, reported on original research and presented findings or commentary relevant to rural definition of health, were published over the last 40 years, and were based on observations of rural U.S., Canadian, or Australian populations. Two reviewers were assigned to each selected article and blinded to the other reviewer's comments. For discordant reviews, a third blinded review was performed. RESULTS: Of the 125 published articles identified from the literature, 34 included commentary or findings relevant to a rural definition of health. Of these studies, 6 included an urban comparison group. Few studies compared rural and urban definitions of health directly. Findings relevant to rural definition of health covered a broad range; however, good health was commonly characterized as being able to work, reciprocate in social relationships, and maintain independence. This review largely confirmed many general characteristics on rural views of health, but also documented the extensive methodological limitations, both in terms of quantity and quality, of studies that empirically compare rural vs. urban samples. Most notably, the evidence base in this area is weakened by the frequent absence of parallel comparison groups and standardized assessment tools. CONCLUSIONS: To engage and activate rural patients in their own healthcare, a better understanding of the health beliefs in rural populations is needed. This review suggests that rural residents may indeed hold distinct views on how to define health, but more rigorous studies are needed to confirm these findings.
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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 | 0.021 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".