Brief Screen to Identify 5 of the Most Common Forms of Psychosocial Distress in Cardiac Patients
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a brief psychosocial screening tool (Screening Tool for Psychological Distress [STOP-D]) for use in the outpatient cardiology setting. BACKGROUND: Psychosocial factors contribute significantly to the morbidity and mortality associated with coronary artery disease. Yet, it is often considered overly burdensome to implement full-scale psychological assessments for every patient. METHODS: Over 3 months, 194 cardiac patients were consecutively recruited from 3 cardiac clinics: heart transplant (pre and post), cardiac rehabilitation, and adult congenital heart. Subjects filled out a questionnaire that included: (1) demographics, (2) STOP-D, (3) Beck Depression Inventory-II, (4) Beck Anxiety Inventory, (5) State-Trait Anger Expression Inventory-2, and (6) MOS Social Support Survey. RESULTS: Analyses reveal all STOP-D items are highly correlated with the corresponding measures and have robust receiver operating characteristic curves. Severity scores on STOP-D-depression and STOP-D-anxiety correlate well with established severity cutoff scores on the Beck Depression Inventory and the Beck Anxiety Inventory, respectively. CONCLUSIONS: Overall, the STOP-D performs very well when compared with other longer and validated measures. The STOP-D is a 5-item self-report measure, which provides severity scores for: depression, anxiety, stress, anger, and poor social support. The STOP-D is self-administered and takes between 1 and 2 minutes to fill out, gives valid severity scores on 5 key areas of psychological distress (depression, anxiety, stress, anger, and poor social support), requires no scoring, and is free to use.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".