Care delivery in the context of district mental healthcare plans in Ghana: a qualitative study exploring experiences of primary healthcare workers and service users
Bibliographic record
Abstract
OBJECTIVE: To explore the perceptions and experiences of mental health service users and healthcare workers regarding the implementation of district mental healthcare plans (DMHPs) in three district demonstration sites in Ghana. DESIGN: The study employed a qualitative design using reflexive thematic analysis. Interview data were analysed by combining inductive and deductive approaches. SETTING: The study was conducted in three DMHP districts in Ghana: Anloga (Volta), Asunafo North (Ahafo) and Bongo (Upper East). The districts were selected via national stakeholder consultations, using a DMHP framework. Data were collected between January 2023 and June 2023. PARTICIPANTS: In-depth interviews were conducted with 28 primary healthcare workers who played key roles in the delivery of care in the demonstration districts. Thirty-two service users who are 18 years and above and have been receiving healthcare for the past year in the demonstration districts were also interviewed. Participants were purposively sampled. FINDINGS: Three main themes were identified: (1) factors supporting DMHP implementation, including capacity building, collaboration, awareness creation and acceptability; (2) challenges affecting DMHP implementation, such as inadequate resources and medication shortages and (3) perceived outcomes of the DMHPs, including improved well-being and daily functioning as well as changing attitudes towards mental health. Some district-level variations were noted in the intensity of challenges and outcomes. CONCLUSION: The DMHPs have shown promise in improving mental healthcare in primary care settings in Ghana. However, addressing resource constraints and medication shortages and sustaining capacity building and awareness creation efforts will be crucial for successful scale-up. The perspectives of service users and healthcare providers offer valuable insights for policymakers and practitioners aiming to enhance integrated mental healthcare.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".