A European Approach to Rural—Urban Differences in Mental Health: The ESEMeD 2000 Comparative Study
Bibliographic record
Abstract
OBJECTIVE: The study aimed to answer the following questions: Are there any rural-urban differences in mental health, once sociodemographic variables are controlled for, and are any of these differences observed in EU countries? Did the individuals suffering from mental health disorders have the same characteristics in rural and urban areas, particularly concerning self-reported impairment? METHOD: The European Study of the Epidemiology of Mental Disorders (ESEMeD 2000 study) is a cross-sectional, in-person, household interview survey based on probability samples representative of the adult population of 6 European countries: Belgium, France, Germany, Italy, the Netherlands, and Spain. The rural population is defined as those living in towns with fewer than 10,000 inhabitants, and the urban population is defined as those living in towns or cities with 10,000 or more inhabitants. A stratified, multistage, random sample without replacement was drawn in each country. The overall response rate of the study was about 61.2% (weighted response rate). RESULTS: The study results confirmed previous findings on the variation in mood disorders between rural and urban areas. Overall, urbanicity seemed to be linked to a higher risk of mental health disorders, particularly depressive disorders, whereas the link to anxiety disorders was only moderate and there was no link at all to alcohol disorders. Country differences concerned male respondents and not female respondents, with the exception of Belgium, where the differences concerned women only (and showed fewer disorders in rural areas). CONCLUSIONS: This study will, hopefully, stimulate further intra-European studies using comparable methods and instruments to look at the experience across the European continent and introduce steps to harmonize rural-urban population limits across diverse countries.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".