Correlation between the implementation of Psychosocial Care Centers and the rates of psychiatric hospitalizations and suicide in Porto Alegre-RS from 2008 to 2018
Bibliographic record
Abstract
INTRODUCTION: The Brazilian psychiatric reform has revolutionized the way that mental health care is provided all over the country, introducing the Psychosocial Care Centers (CAPS) and encouraging care at liberty. The CAPS have been assigned many objectives, such as prevention of hospitalizations and intervention in crises or suicide. This paper aims to describe the correlation between the implementation of CAPS and the rates of psychiatric hospitalizations and suicides from 2008 to 2018. METHODS: This study has an ecological time series design and included residents of the city of Porto Alegre, RS, Brazil, who were hospitalized through the Sistema Único de Saúde (SUS). The data were obtained from official databases (DATASUS, CNES, and IBGE) and indicators were calculated (CAPS coverage, hospitalization rate, and suicide rate). Associations between the indicators were tested using Pearson's correlation coefficients. RESULTS: We found a negative correlation between provision of CAPS and psychiatric hospitalizations (r = -0.607 p = 0.048). CONCLUSION: These results support the hypothesis that there is a negative correlation between implementation of the CAPS and psychiatric hospitalizations. This reinforces the importance of implementing policies related to improving psychiatric reform.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".