Recovering from spontaneous coronary artery dissection: Patient-reported challenges and rehabilitative intervention needs.
Bibliographic record
Abstract
BACKGROUND: Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of acute coronary syndrome that disproportionally affects younger women. The underlying etiology is incompletely understood, postmorbid psychological distress is high, and treatment plans are predominantly based on clinician experience. There remains uncertainty on how to adequately address the needs of patients with SCAD as part of secondary prevention. METHOD: As a Define and Refine phase of the ORBIT model (Phase 1), this study investigated SCAD patients' challenges and rehabilitative intervention needs using a qualitative research design. Patients with SCAD were purposively recruited to participate in structured interviews that were analyzed using inductive thematic coding techniques. RESULTS: Patients with SCAD (n = 15; 86.7% female; mean age = 47.5 years; data saturation reached with patient sample) expressed challenges in (a) navigating uncertainty associated with the disease; (b) living with anxiety; (c) reconciling pre and post-SCAD identities; (d) accurately identifying symptoms and experiencing a sense of isolation in recovery due to gender and young age; and (e) managing changing family dynamics and family members' stress. Intervention needs included (a) addressing unique demographic and cardiovascular profiles when designing programs for cardiac rehabilitation; (b) providing more psychological and peer support resources to address anxiety and sense of isolation; (c) disseminating information on rapidly evolving SCAD research; and (d) acknowledging and providing support to the family system. CONCLUSIONS: The results signal curricula to be included in tailored SCAD programming and underscore the need for further study and dissemination of optimal secondary preventative care for this patient population. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".