Improving Mental Health Knowledge Among Brazilian Community Health Agents in a Training Program for a Self‐Reported Mental Health Assessment
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate a mental health training process that included the implementation of the interRAI Self-Reported Assessment for Mental Health (SAMH) tool among Community Health Agents (CHAs), to improve knowledge of mental health issues, as well as to assess the tool's feasibility and acceptability. METHOD: This is a quasi-experimental study. The participants were 24 CHAs from a municipality in the Metropolitan Region of Porto Alegre, Rio Grande do Sul, Brazil. The instruments used were a sociodemographic and work-related questionnaire; a pre-test and post-test questionnaire to assess the knowledge of mental health, and a questionnaire assessing the feasibility and accessibility of interRAI SAMH, which was part of the training process. To compare knowledge before and after the process, a paired-sample t-test was used. Feasibility and acceptability of the SAMH were evaluated using a questionnaire and qualitative data. RESULTS: The training, combined with the SAMH data collection process, contributed to increased perceived knowledge of the following topics: psychiatric reform, human rights in mental health, Mental Health National Policy, Mental Health Care Models, Psychiatric Reform Law, risk factors for suicide, depression, alcohol and drugs, personalized therapeutic plan, psychoses, anxiety disorders, psychiatric urgences and emergencies, and interdisciplinary work. The results of feasibility and acceptability evidenced that CHAs feel more capable of recognizing psychiatric pathologies and emergencies, such as individuals at risk of suicide. CONCLUSION: The findings support the feasibility and acceptability of the interRAI SAMH tool and its potential to enhance CHAs' knowledge and ability to identify and refer individuals with mental health conditions in Primary Health Care.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".