Psychiatric screening tools in asthma patients: a systematic review
Bibliographic record
Abstract
Introduction: Psychiatric comorbidities, including anxiety, depression, and posttraumatic stress disorder (PTSD), are 1.5 to 2.4 times more prevalent in individuals with asthma than in the general population. Despite their significant impact on disease outcomes, these conditions remain underdiagnosed due to barriers such as time constraints, lack of routine screening protocols, and limited accessibility of validated tools. As a result, screening remains limited. Objective: This systematic review evaluates the efficacy, feasibility, and cost of psychiatric screening tools used in asthma populations. The aim is to identify practical options for clinical implementation and assess potential barriers to their adoption. Methods: A search was conducted across MEDLINE, EMBASE, SCOPUS, CINAHL, and Web of Science for studies published before July 25, 2024. Costs and copyright/accessibility were assessed through internet searches and direct communication. Original validation studies of the tools were reviewed. Results: A total of 23 studies met the inclusion criteria, identifying eight validated psychiatric screening tools for anxiety, depression, and PTSD in asthma populations. These tools were effective in detecting comorbidities, even in undiagnosed patients. Three tools are freely available, while five require a fee. Conclusion: Given a multipartite set of parameters that includes ease of administration, effectiveness, and cost, we recommend a combination of the PHQ-9, GAD-7, and PCL-5 as optimal screening tools for psychiatric comorbidity in primary care and specialty clinics managing adult asthma. Two of these tools are accessible through Alberta’s Epic-based electronic medical record (EMR) system, Connect Care.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".