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 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 | 0.026 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".