A situational analysis of primary health care centers in Brazil: challenges and opportunities for addressing mental illness and substance use-related stigma
Bibliographic record
Abstract
BACKGROUND: The detrimental impact of stigma toward people with mental illness and substance use problems (MISUP) is well documented. However, studies focusing on stigma reduction in Latin American primary health care (PHC) contexts are limited. This situational analysis incorporating a socioecological framework aims to provide a comprehensive understanding of MISUP-related stigma in PHC centers in Brazil. The objectives of this analysis are twofold: (1) to understand the current mental health and substance use service delivery context and (2) identify challenges and opportunities for addressing MISUP-related stigma in PHC centers in Ribeirão Preto, Brazil. METHODS: Environmental scans of four Family Health Units were conducted in early 2018 to explore population needs and service delivery for individuals with MISUP. In addition, a symposium was organized in October 2018 to consult with diverse stakeholders and gather local perspectives about MISUP-related stigma conveyed in PHC settings. NVivo 12 software was used to conduct a thematic analysis of the qualitative data collected from the environmental scans and the symposium consultation. RESULTS: Themes identified at the national level in the socioecological framework indicate that political support for national policies related to reducing stigma is limited, particularly regarding social inclusion and the decentralization of mental health services. Themes at the regional, organizational, and interpersonal levels include insufficient mental health expertise and the limited involvement of those with lived experience in decision-making. Suggestions for stigma interventions were provided, including increased contact with individuals with lived experience outside of client-patient interactions, capacity building for professionals, and public education campaigns. CONCLUSION: Increased government support, capacity building, and promoting social inclusion will provide opportunities to reduce stigma and reach marginalized populations. These findings will assist with addressing current gaps in PHC mental health service provision and may inform anti-stigma strategies for Brazil and other Latin American low- and middle-income countries.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".