Examining the psychometric properties of the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 among young urban South African women
Bibliographic record
Abstract
BACKGROUND: Valid, reliable, and easy-to-administer scales are crucial for identifying mental health conditions, especially in LMICs where such scales tend not to be validated. This study aims to address this gap by investigating the psychometric properties and factorial structure of the PHQ-9 and GAD-7 in a sample of young women in Soweto, South Africa. METHODS: The PHQ-9 and GAD-7 were administered to 6028 women aged 18-28 years old. Cronbach's alpha, Mokken scale analysis, and Confirmatory Factor Analysis were used to provide support for the internal consistency and construct validity of these scales. RESULTS: Both scales demonstrated good internal consistency (α = 0.81 for PHQ-9 and α = 0.84 for GAD-7). Internal consistency reliability was further supported by positive inter-item correlations and item-by-scale correlations for all items on both measures. CFA of the PHQ-9 and GAD-7 showed a reasonable fit for the 1-factor model and 2-factor models (depression and anxiety with somatic and cognitive subtypes). LIMITATIONS: This study was limited to young African women in urban Soweto who were proficient in English, which may affect generalizability. Differences in language or cultural context may impact the accuracy and applicability of these scales to other African populations. CONCLUSION: The PHQ-9 and GAD-7 are valid and reliable for identifying psychological distress in the studied population. Despite showing good psychometric properties, further diagnostic assessment is needed to confirm clinical diagnoses. The scales are useful for identifying those at risk but not a substitute for comprehensive diagnostic evaluations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".