Prevalence and correlates of depressive symptoms among prisoners in Kaliti Federal Prison in Ethiopia: a facility based cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: This study intended to examine the prevalence and correlates of depressive symptoms among inmates in Kaliti Prison Centre, Addis Ababa, Ethiopia. METHODS: A facility-based cross-sectional study was conducted among 694 randomly selected inmates in Kaliti Federal Prison in Ethiopia. The depressive symptom was examined using the Patient Health Questionnaire (PHQ-9). A binary logistic regression model was fitted to identify correlates of depressive symptoms. A p value <0.05 was considered to declare statistical significance, and an adjusted OR (AOR) with the corresponding 95% CI was computed to determine the strength of association. Data were analysed using SPSS V.20. RESULT: The prevalence of depressive symptoms among prisoners in the current study was 56.6% (95% CI 53.2 to 60.8). Poor social support (AOR: 3.33, 95% CI 2.03 to 5.458), personal history of mental illness (AOR=3.16, 95% CI 1.62 to 6.14), physical abuse (AOR=2.31, 95% CI 1.41 to 3.78) and comorbid chronic medical illness (AOR=3.47, 95% CI 2.09 to 5.74) were independent correlates of depressive symptoms. CONCLUSION: Our study shows that around one in two prisoners screened positive for depressive symptoms. There should be a regular screening of depressive symptoms for prisoners, and those screened positive should be linked to proper psychiatric service for early diagnosis and treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".