Analyses of Prevalence of Mental Illness and Associated Characteristics in Anambra State
Bibliographic record
Abstract
This study on the prevalence of mental illness and associated characteristics in Anambra State was aimed at determining the prevalence of mental illness by age, sex and period of the year. It was also aimed at determining the relationship between mental illness diagnosis and the studied characteristics. Descriptive statistics, chi-square test of association and the ordinal logistic regression were employed in the study. The results presented show that a significant association exist between mental illness and sex. Also a significant association was found to exist between mental illness and age of patients. This was also visible in the pattern of manifestation of mental illness as mental illness was found to reduce as age increases. On the average, the second quarter of the year was found to have the highest prevalence of mental illness in the study area. On the other hand, even when the first and second quarter proved to be significantly related with mental illness diagnosis, the chi-square test of association shows that there is no significant association between mental illness diagnosis and the period of the years the diagnosis was made. In conclusion, mental illness was found to have significant effect on demographic characteristics but partially with seasonal variations.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".