Depression and Its Help Seeking Behaviors: A Systematic Review and Meta-Analysis of Community Survey in Ethiopia
Bibliographic record
Abstract
Background. Depression is one of the most common mental illnesses affecting around 322 million individual in the world. Although the prevalence of depression is high and its treatment is effective, little is known about its pooled prevalence and help seeking behaviors in the community settings of Ethiopia. Thus, this study aimed to determine the pooled prevalence of depression and its help seeking behaviors in Ethiopia. Methods. A systematic literature search in the databases of Pub-Med, Cochrane, and Google Scholar was performed. The quality of studies was assessed using the Newcastle-Ottawa quality assessment tool adapted for cross-sectional studies. Heterogeneity test and evidence of publication bias were assessed. Moreover, sensitivity test was also performed. Pooled prevalence of depression and its help seeking behavior were calculated using random effects model. Results. A total 13 studies for depression, 4 studies for help seeking intention, and 5 studies for help seeking behaviour were included in this review. The pooled prevalence of depression and help seeking intention and behaviour was found to be 20.5% (95% CI; 16.5% -24.4%), 42% (95% CI; 23%-60%), and 38% (95% CI; 23%-52%), respectively. There is no significant heterogeneity for depression (I2 = 0%, p =0.620), help seeking intention (I2 = 0%, p =0.996), and behaviour (I2 = 0%, p =0.896). There is no publication bias for depression egger’s test (p =0.689). Conclusion. More than one in every five individuals were experiencing depression. Less than one-third of individuals with depression seek help from modern treatment. Authors suggest community based mental health screening 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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".