Psychometric properties of the PHQ-9 measure of depression among Brazilian older adults
Bibliographic record
Abstract
Objectives: To obtain evidence on the psychometric properties of the Patient Health Questionnaire − 9 (PHQ-9, one of the most extensively used tools for assessing depression) in the Brazilian older population.Method:Data on 3,356 Brazilian adults aged 60+ years living in Guarulhos, São Paulo state were used. The factor structure of the questionnaire was analysed using a factor analysis approach. The questionnaire’s measurement equivalence was tested across gender, age, personal income, and education level groups. The scores were compared across groups based on the highest level of equivalence achieved. The questionnaire’s internal consistency was analysed considering its factor structure.Results:A one-factor solution was identified as the most adequate factor structure, with the factor explaining 57.6% of the items’ variance. The correlation of the resulting latent score with the overall raw sum score in the PHQ-9 was r = 0.96. Measurement equivalence regarding thresholds and loadings was achieved for all tested groups. On average, women, older, less educated, and poorer people had higher latent scores on the depression factor. The measure showed a good internal consistency with Revelle’s omega total ωt=0.92.Conclusion:The results suggest that, among Brazilian older adults living in Guarulhos, São Paulo state, the PHQ-9 measures depressive symptomatology equivalently across different sociodemographic subgroups. Moreover, it can be scored using the raw sum of the item scores to adequately reflect different levels of depressive symptomatology.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".