L’intégration sociale protège-t-elle vraiment contre la solitude ? Déterminants genrés dans une population rurale du Sénégal
Bibliographic record
Abstract
INTRODUCTION: This study aims to test a measure of loneliness and to document its determinants among rural men and women in Senegal. METHODS: Data from the Niakhar Social Networks and Health Project were used. The analysis sample was composed of 1261 residents aged 16 years and older. Analyses were stratified by gender. Associations between loneliness and its determinants (socio-demographic characteristics and level of social integration) were examined with multivariate logistic regressions. RESULTS: Loneliness affects almost one in three people. Its prevalence is more significant for women. Multivariate analyses indicate that for both men and women, older age intensifies loneliness and recent migration experience protects against loneliness. Other factors act differently according to gender. Widowhood or divorce for men, and residential isolation for women, worsen the experience of loneliness. Social integration protects men against loneliness, but this relationship is not found for women. Finally, the effect of the level of social integration on loneliness varies with age. CONCLUSIONS: This study, which documents a phenomenon which is often neglected by misconceptions about social solidarities in these societies, suggests that loneliness is not linked to the same issues for men and women. For men, being socially integrated and being in a union are protective, whereas for women, poor social integration does not appear to be a clear source of loneliness, unlike residential isolation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".