Factor structure of the patient health questionnaire-9 and measurement invariance across gender and age among Chinese university students
Bibliographic record
Abstract
The Patient Health Questionnaire-9 (PHQ-9) has been widely used to screen depression symptoms. The present research aimed to assess the reliability and validity of PHQ-9, besides measurement invariance of the PHQ-9 across gender and age among Chinese university students. A total of 12,957 Chinese college students from 2 universities in Henan and Hainan provinces (China) completed the questionnaires via WeChat. This research reported the psychometric properties of PHQ-9 and measurement invariance of the PHQ-9 across gender and age among Chinese university students. Compared with 1-factor model, the 2-factor (affective factor and somatic factor) model of PHQ-9 showed a better fit index in Chinese university students. Without the last 2 items, the 2-factor model of the PHQ-9 showed satisfactory reliability, validity, and good fit index (e.g., Root mean square error of approximation = 0.060, Goodness-of-fit index = 0.982, Comparative fit index = 0.986, and Tucker-Lewis index = 0.974). The Cronbach's alpha of PHQ-9 was 0.874. Multi-group analysis across gender and age demonstrated that measurement equivalency for the 2-factor model of the PHQ-9 was established (e.g., Root mean square error of approximation < 0.08, Comparative fit index > 0.90 and Tucker-Lewis index > 0.90). The 2-factor model of the PHQ-9 without the items of "movement" and "desire to die" showed a better fit index in Chinese university students. The measurement equivalence across gender and age for the 2-factor model of the PHQ-9 can be established among Chinese university students.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".