An analysis of patterns and predictors of self-reported common mental disorders in Ibadan Metropolis, Nigeria
Bibliographic record
Abstract
Common mental disorders (CMDs) have been on the rise in developing countries. This study set out to unravel the pattern of CMD prevalence in a traditional African city, Ibadan. The study, in addition to socio-economic and demographic variables, takes into cognisance the effect of some peculiar environmental variables. The Self-Reporting Questionnaire-20 was used for CMD screening, and the questionnaire was administered to 1,200 respondents in a cross-sectional survey approach. The results showed that the overall pattern of CMD prevalence is random (Global Moran’s I (P = 0.78, I = 0.00 and Z = 0.29)). Respondents without education reported the highest cases of CMD (48.6%). When combined together, migrants reported 52.5% of the CMDs. The significant variables are food security (β = −0.198), green space (β = −0.057), migration status (β = −0.054), flood-prone residence (β = 0.453), low-quality housing (β = −0.061), frequent recreation participation (β = −0.071), experience of spousal violence (β = 0.199), positive self-rated health (β = −0.134) and positive quality of life (β = −0.205). The predictors of CMD explained about 35.8% of the variation (R2) and an R value of 59.9%. The study showed that CMDs occur among most of the urban population. Adequate media sensitization will have significant ameliorating effects on urban residents.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".