interRAI Quality of Life for Mental Health and Addictions: Psychometric Properties and Differences Across Age, Gender, and Service Settings in Brazil
Bibliographic record
Abstract
OBJECTIVE: Estimate the psychometric properties of the interRAI Quality of Life for Mental Health and Addictions (interRAI QOL) instrument with users of Psychosocial Care Centers and participants of therapeutic groups in Primary Health Care, exploring age, gender, and service settings differences in quality of life. METHOD: This quantitative study was conducted with 617 users from Psychosocial Care Centers and Primary Care services in two Brazilian states, Rio Grande do Sul and Rondônia. Data collection was carried out using the interRAI QOL. Confirmatory factor analysis and reliability assessment were performed using McDonald's Omega index. Non-parametric tests, including Mann-Whitney and Kruskal-Wallis, were conducted to compare the Quality-of-Life dimensions among participants based on age, gender, and care unit. RESULTS: The confirmatory factor analysis indicated a good fit for the hypothesized model (CFI = 0.97, RMSEA = 0.08). Reliability was adequate for all subscales according to McDonald's Omega, ranging from 0.71 to 0.88. Gender differences were observed in the well-being and health dimensions, while all dimensions except support showed significant differences based on age group. The care unit location also revealed significant differences across all dimensions. Participants from Psychosocial Care Center Alcohol and Drugs and from Primary Health Care show better QOL profiles than in other settings and regions. CONCLUSION: The interRAI QOL demonstrated adequate psychometric properties and proved to be a valuable new instrument for assessing quality of life among individuals receiving care in the psychosocial care network.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".