Epidemiological patterns of mental disorders and stigma in a community household survey in urban slum and rural settings in Kenya
Bibliographic record
Abstract
PURPOSE: This study investigated the epidemiological patterns of mental illness and stigma in community households in Kenya using a cross-sectional community household survey among 846 participants. METHODS: A cross-sectional community household survey was conducted around urban slum (Kangemi) and rural (Kibwezi) selected health facilities in Kenya. All households within the two sites served by the selected health facilities were included in the study. To select the main respondent in the household, the oldest adult who could speak English, Kiswahili or both (the official languages in Kenya) was selected to participate in the interview. The Opinion about Mental Illness in Chinese Community (OMICC) questionnaire and the MINI-International Neuropsychiatric Interview-Plus Version 5 (MINI) tools were administered to the participants. Pearson's chi-square test was used to compare prevalence according to gender, while adjusted regression models examined the association between mental illness and views about mental illness, stratified by gender. RESULTS: The overall prevalence of mental illness was 45%, showing gender differences regarding common types of illness. The opinions about mental illness were similar for men and women, while rural respondents were more positively opinionated than urban participants. Overall, suffering from mental illness was associated with more positive opinions among women and more negative opinions among men. CONCLUSION: More research is needed into the factors explaining the observed differences in opinion about mental illness between the subgroups, and the impact of mental illness on stigma in Kenya in order to create an evidence-based approach against stigma.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".