Policy, power, stigma and silence: Exploring the complexities of a primary mental health care model in a rural South African setting
Bibliographic record
Abstract
The Movement for Global Mental Health's (MGMH) efforts to scale up the availability of mental health services have been moderately successful. Investigations in resource-poor countries like South Africa have pointed to the value of an integrated primary mental health care model and multidisciplinary collaboration to support mental health needs in underserved and underresourced communities. However, there remains a need to explore how these policies play out within the daily realities of communities marked by varied environmental and relational complexities. Arguably, the lived realities of mental health policy and service delivery processes are best viewed through ethnographic approaches, which remain underutilised in the field of global mental health. This paper reports on findings from a case study of mental health services for HIV-affected women in a rural South African setting, which employed a motivated ethnography in order to explore the realities of the primary mental health care model and related policies in South Africa. Findings highlighted the influence of three key symbolic (intangible) factors that impact on the efficacy of the primary mental health care model: power dynamics, which shaped relationships within multidisciplinary teams; stigma, which limited the efficacy of task-shifting strategies; and the silencing of women's narratives of distress within services. The resultant gap between policy ideals and the reality of practice is discussed. The paper concludes with recommendations for building on existing successes in the delivery of primary mental health care in South Africa.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.049 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".