Is the health of people living in rural areas different from those in cities? Evidence from routine data linked with the Scottish Health Survey
Bibliographic record
Abstract
BACKGROUND: To examine the association between rurality and health in Scotland, after adjusting for differences in individual and practice characteristics. DESIGN: Mortality and hospital record data linked to two cross sectional health surveys. SETTING: Respondents in the community-based 1995 and 1998 Scottish Health Survey who consented to record-linkage follow-up. MAIN OUTCOME MEASURES: Hypertension, all-cause premature mortality, total hospital stays and admissions due to coronary heart disease (CHD). RESULTS: Older age and lower social class were strongly associated with an increased risk of each of the four health outcomes measured. After adjustment for individual and practice characteristics, no consistent pattern of better or poorer health in people living in rural areas was found, compared to primary cities. However, individuals living in remote small towns had a lower risk of a hospital admission for CHD and those in very remote rural had lower mortality, both compared with those living in primary cities. CONCLUSION: This study has shown how linked data can be used to explore the possible influence of area of residence on health. We were unable to find a consistent pattern that people living in rural areas have materially different health to that of those living in primary cities. Instead, we found stronger relationships between compositional determinants (age, gender and socio-economic status) and health than contextual factors (including 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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".