Investing in mental health in Somalia: harnessing community mental health services through task shifting
Bibliographic record
Abstract
Background: The increase of mental health issues globally has been well documented and now reflected in the United Nations' Sustainable Development Goals as a matter of global health significance. At the same time, studies show the mental health situations in conflict and post-conflict settings much higher than the rest of the world, lack the financial, health services and human resource capacity to address the challenges. Methods: The study used a descriptive literature review and collected data from public domain, mostly mental health data from WHO's Global Health Observatory. Since there is no primary database for Somalia's public health research, the bibliographic databases used for mental health in this study included Medline, PubMed, CINAHL, PsycINFO, and Google Scholar. Results: The review of the mental health literature shows one of the biggest casualties of the civil war was loss of essential human resources in healthcare as most either fled the country or were part of the victims of the war. Conclusion: In an attempt to address the human resource gap, there are calls to task-shift so that available human resource can be utilized efficiently and effectively. This policy paper discusses the case of Somalia, the impact of decade-long civil conflict on mental health and health services, the significant gap in mental health service delivery and how to strategically and evidently task-shift in closing the mental health gap in service delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".