Validation of the Patient Health Questionnaire-2 to screen for depression in Canadian Armed Forces personnel
Bibliographic record
Abstract
Post-deployment screening within the Canadian Armed Forces (CAF) aims to capture those with mental health issues so that appropriate and timely treatment can be provided. However, the process is lengthy and places considerable burden on CAF members and clinicians. Evaluation of shorter measures of mental health is an important step toward reducing the length of the process while still capturing those in need of care. This study evaluated the 2-item Patient Health Questionnaire (PHQ-2) as a potential brief measure of depression to be included in screening. Operating characteristics of the PHQ-2 were assessed against the full scale using existing recommended cutoffs, as well as clinician impressions of depression being of major concern. Correlations of the PHQ-2 with other measures of health were also examined. The PHQ-2 demonstrated good sensitivity and specificity for detecting depression compared to the full scale and to clinician impressions, at cutoffs similar to those found in past research. As well, it exhibited high correlations with other measures of mental health. This study provides evidence for the validity of the PHQ-2 as a brief screening tool for depression in CAF members following deployment.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".