The factor structure of the Patient Health Questionnaire-9 in stroke: A comparison with a non-stroke population
Bibliographic record
Abstract
BACKGROUND: It is unclear if certain post-stroke somatic symptoms load onto items of the Patient Health Questionnaire-9 (PHQ-9), a self-report depression questionnaire. We investigated these concerns in a stroke sample using factor analysis, benchmarked against a non-stroke comparison group. METHODS: The secondary dataset constituted 787 stroke and 12,016 non-stroke participants. A subsample of 1574 comparison participants was selected via propensity score matching. Dimensionality was assessed by comparing fit statistics of one-factor, two-factor, and bi-factor models. Between-group differences in factor structure were explored using measurement invariance. RESULTS: A two-factor model, consisting of somatic and cognitive-affective factors, showed better fit than the unidimensional model (CFI = 0.984 versus CFI = 0.974, p < .001), but the high correlation between the factors indicated unidimensionality (r = 0.866). Configural invariance between stroke and non-stroke was supported (CFI = 0.983, RMSEA = 0.080), as were invariant thresholds (p = .092) and loadings (p = .103). Strong invariance was violated (p < .001, ΔCFI = -0.003), stemming from differences in the tiredness and appetite intercepts. These differences resulted in a moderate overestimation of depression in stroke when using a summed score approach, relative to the comparison sample (Cohen's d = 0.434). CONCLUSIONS: The findings suggest that the PHQ-9 measures a single factor in stroke. Because stroke patients may report higher tiredness on item 4, caution is advisable when classifying patients as depressed if they are near the cut-off and have significant post-stroke fatigue. Caution is also advised when comparing total scores between stroke and other populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".