Development and Implementation of Mental Healthcare Plans in Three Districts in Ghana: A Mixed-Method Process Evaluation Using Theory of Change
Bibliographic record
Abstract
In Ghana, a severe mental healthcare gap of 95-98% exists due to limited services. Ghana Somubi Dwumadie set out to address this by developing district mental healthcare plans (DMHPs) in three demonstration districts. Following the Programme for Improving Mental Healthcare model, district mental health operations teams were formed and used Theory of Change (ToC) to develop DMHPs. Key elements included training non-specialist health workers and enrolling individuals in relevant healthcare programmes. Evaluation methods included routine data, health facility surveys, and qualitative analysis within the ToC framework. Results showed improved integration of mental health services, enhanced case management through training, and increased service utilisation, shown through 691 service user enrollments. However, there was limited commitment of new resources and no significant improvement in primary care workers' capacity to detect priority mental health conditions. The study concludes that DMHPs, implemented with an integrated approach, can improve mental health service utilisation, contingent on committed leadership, resource availability, and stakeholder engagement.
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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 | 0.038 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".