Collective resources or local social inequalities? Examining the social determinants of mental health in rural areas
Bibliographic record
Abstract
BACKGROUND: In England, although some studies report better health among rural populations, few have examined social inequalities in health within rural areas and how they compare to inequalities observed in urban settings. The objectives of this study are to examine (i) whether living in rural, in more affluent and in more socially cohesive areas is associated with better mental health; and (ii) whether being in employment is more protective for mental health in rural than in urban areas. METHODS: Data on common mental disorders (CMD) and socio-demographic characteristics of 12 962 adults are from the Health Survey for England. Individuals resided in 892 areas categorized as urban or rural. Area deprivation is measured using the employment deprivation domain from the 2004 Index of Multiple Deprivation. Area social cohesion is derived from individuals' perceptions using ecometric procedures. Data are analysed using multilevel logistic models. RESULTS: Living in rural areas is significantly associated with lower risk of reporting CMD [odds ratio (OR): 0.81; 95% confidence interval (CI): 0.71-0.92], independently of individuals' characteristics, and of area deprivation and social cohesion. The mental health advantage of being in employment is more important in rural areas (OR: 0.74; 95% CI: 0.58-0.95) than in urban settings. CONCLUSION: Living in rural areas is associated with better overall mental health. Yet inequalities in mental health between people in the workforce and those who are not are more important in rural settings. More studies are needed to understand the patterning of social inequalities in health in rural communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".