At the coalface of collaborative mental health care: A qualitative study of governance and power in district‐level service provision in South Africa
Bibliographic record
Abstract
Globally, there is an urgency to address fragmented mental health systems, especially in low-to-middle income countries. State and non-state mental health service collaboration is a central strategy to strengthen care. The study was undertaken to analyse the power in governance processes of public mental health service provision. Semi-structured interviews were conducted with state and non-state actors in mental health care in a South African district. Transcriptions were thematically analysed using the Framework for Assessing Power in Collaborative Processes. Findings suggested that collaborative processes were significantly state-owned, in terms of funding models, administrative and legislative jurisdiction, and state hierarchical referral structure. No formal agreements were in place, elevating the importance of key network actors to bring less-endowed NGOs into the service network. Fragmentation between the Departments of Health and Social Development was telling in district forums. Resistance to power structures unfolded, some participants sidestepping traditional hierarchies to leverage funding and support. The paper highlights the complexities and different facets of power in integrated mental health care in a South African district, adding to growing literature on the social mechanisms that influence collaboration.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".