Brief Screen to Identify 5 of the Most Common Forms of Psychosocial Distress in Cardiac Patients
Why this work is in the frame
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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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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 it