Systematic review of pathways to mental health care in Brazil: narrative synthesis of quantitative and qualitative studies
Bibliographic record
Abstract
BACKGROUND: Pathways to care are actions and strategies employed by individuals in order to get help for health-related distress and the related processes of care providers. On several systematic reviews regarding pathways to mental health care (PMHC), studies regarding South American countries were not present. This review synthesizes qualitative and quantitative research about PMHC in Brazil. METHODS: LILACS, MEDLINE and SCIELO databases were searched for papers regarding PMHC in Brazil. The results were organized in pathway stages, based on Goldberg and Huxley's 'model of Levels and Filters' and on Kleinman's framework of 'Popular, Folk and Professional health sectors'. Analysis also considered the changes in national mental health policy over time. RESULTS: 25 papers were found, with data ranging from 1989 to 2013. Complex social networks were involved in the initial recognition of MH issues. The preferred points of first contact also varied with the nature and severity of problems. A high proportion of patients is treated in specialized services, including mild cases. There is limited capacity of primary care professionals to identify and treat MH problems, with some improvement from collaborative care in the more recent years. The model for crisis management and acute care remains unclear: scarce evidence was found over the different arrangements used, mostly stressing lack of integration between emergency, hospital and community services and fragile continuity of care. CONCLUSIONS: The performance of primary care and the regulation of acute demands, especially crisis management, are the most critical aspects on PMHC. Although primary care performance seems to be improving, the balanced provision and integration between services for adequate acute and long-term care is yet to be achieved.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".