Screening tools for ruling out mood and anxiety disorders in adults in primary care: a rapid systematic review
Bibliographic record
Abstract
BACKGROUND: Patients with mood and anxiety disorders commonly visit primary care providers in Canada. Screening tools can support providers in identifying patients who need further mental health care. OBJECTIVES: Identify screening tools that are valid and reliable for ruling out mood and anxiety disorders in adults in primary care settings. DATA SOURCES: Our rapid review searched MEDLINE, Embase and PsycInfo from January 1, 2006, to May 27, 2025. STUDY SELECTION: A single reviewer conducted screening, critical appraisal and data extraction. Low risk-of-bias studies were included. Sensitivity, specificity, and negative likelihood ratios (NLRs) with 95% confidence intervals were extracted or calculated. A threshold of NLR < 0.1 was used to interpret strong rule-out performance. SYNTHESIS: We included 11 low risk-of-bias studies evaluating validity in 13 tools. No reliability studies were included due to high risk-of-bias. The Patient Health Questionnaire (PHQ)-2 (≥ 1/7), PHQ-9 (≥ 10/17), Generalised Anxiety Disorder (GAD)-2 (≥ 2/6), and GAD-7 (≥ 5/21) demonstrated the strongest evidence for ruling out depression and anxiety (NLR < 0.1). CONCLUSIONS: The PHQ-2, PHQ-9, HADS-D, 15-item GDS, QIDS-SR16, GAD-2, and GAD-7 are brief, valid tools with strong rule-out performance for depression and anxiety in primary care. Future research should evaluate reliability and performance in diverse providers and patients, including non-physician settings and underserved communities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".