Depression and Anxiety Screening and Triage Protocol for Cardiac Rehabilitation Programs
Bibliographic record
Abstract
PURPOSE: Best practice guidelines for cardiac rehabilitation (CR) suggest routine screening for anxiety and depression, yet many patients are not screened nor do they receive mental health treatment. Protocols are required to identify those in need of care and to ensure that appropriate assistance is provided. METHODS: Consecutive patients entering CR in our setting from May 4, 2012, to May 3, 2013, completed the Hospital Anxiety and Depression Scale (HADS). As per our Screening and Triage protocol for Anxiety and Depression (STAD), patients with high scores (≥16) were referred to a clinical psychologist; those with low scores (<8 for depression and <11 for anxiety) received information about community resources. Patients with moderate scores were reassessed 4 weeks later before triaging to psychosocial services. High, moderate, and low scores were triaged to a clinical psychologist, social worker, or were guided to community resources, respectively. RESULTS: A total of 1504 patients (76% men) completed the HADS at intake; 287 (19%) had elevated depression and/or anxiety scores. Of these, 43 (15%) were referred to psychology services and 244 (85%) patients were referred for HADS readministration at 4 weeks. Scores following reassessment resulted in 6 referrals to psychology services (3%) and 62 to social work (36%), whereas 78 (45%) no longer needed care. CONCLUSION: Many cardiac patients experience symptoms of depression and anxiety. The STAD protocol using the HADS was an efficient method to screen for anxiety and depression and appropriately utilize psychosocial treatment resources in the cardiac rehabilitation setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".