Validation of the Mental Illness: Clinicians’ Attitudes Scale: The Factor Structure and Psychometric Properties of the Brazilian Version
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: In the literature, few instruments have been identified to measure the stigma of health professionals toward people with mental illness. In Brazil, until 2021, the literature did not indicate the validation of an instrument or the construction of an instrument for this purpose. Considering this gap, this study aimed to validate and estimate the reliability of the Mental Illness: Clinicians' Attitudes Scale, version 4 (MICA-4) for the Brazilian context, examining the psychometric properties through the analysis of its internal consistency and factor structure. METHODS: Psychometric testing was completed in a sample of health professionals from Primary HealthCare Units. Reliability analysis was conducted in SPSS v23. Cronbach's Alpha and item total correlation were used. The dimensionality of the MICA was explored using exploratory factor analysis (EFA) in Mplus 8.2. RESULTS: A total of 195 health professionals participated in the research. Cronbach's Alpha was 0.68 and according to the reliability analysis, items 10 and 12 of the original version were deleted, resulting, therefore, in 14 items. In addition, we demonstrated that it is possible to have only two factors instead of five factors, which is the number of factors in the original version of the MICA-4. CONCLUSIONS: This validated instrument for the Brazilian context can serve as an important tool in understanding the phenomenon of the stigma of health professionals toward people with mental illness and, consequently, in promoting anti-stigma strategies in Brazil.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".