Are Scores on English and French Versions of the PHQ-9 Comparable? An Assessment of Differential Item Functioning
Bibliographic record
Abstract
BACKGROUND: Medical research increasingly utilizes patient-reported outcome measures administered and scored in different languages. In order to pool or compare outcomes from different language versions, instruments should be measurement equivalent across linguistic groups. The objective of this study was to examine the cross-language measurement equivalence of the Patient Health Questionnaire-9 (PHQ-9) between English- and French-speaking Canadian patients with systemic sclerosis (SSc). METHODS: The sample consisted of 739 English- and 221 French-speaking SSc patients. Multiple-Indicator Multiple-Cause (MIMIC) modeling was used to identify items displaying possible differential item functioning (DIF). RESULTS: A one-factor model for the PHQ-9 fit the data well in both English- and French-speaking samples. Statistically significant DIF was found for 3 of 9 items on the PHQ-9. However, the overall estimate in depression latent scores between English- and French-speaking respondents was not influenced substantively by DIF. CONCLUSIONS: Although there were several PHQ-9 items with evidence of minor DIF, there was no evidence that these differences influenced overall scores meaningfully. The PHQ-9 can reasonably be used without adjustment in Canadian English- and French-speaking samples. Analyses assessing measurement equivalence should be routinely conducted prior to pooling data from English and French versions of patient-reported outcome measures.
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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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".