Strengthening Transitions in Care for Patients with ST-Elevation Myocardial Infarction: A Theory-Based Qualitative Study
Bibliographic record
Abstract
Background: Despite strong evidence for secondary prevention after ST-elevation myocardial infarction (STEMI), adherence to pharmacotherapy and participation in cardiac rehabilitation remain suboptimal. Fragmented transitions from hospital to outpatient care contribute to early discontinuation, inadequate self-management support, and delayed functional and psychological recovery, particularly in regional systems without standardized follow-up care. This study examined barriers and enablers to post-STEMI transitions in care using a theory-informed qualitative approach. Methods: Semi-structured interviews were conducted with STEMI patients (n = 14), healthcare providers (n = 8), and system leaders (n = 4) within a regional cardiac network in Ontario, Canada. Interview guides were informed by the Theoretical Domains Framework (TDF) and the Consolidated Framework for Implementation Research (CFIR). Data were analyzed using directed content analysis. Results: Patients reported barriers including knowledge gaps about symptoms and treatment (TDF domain: knowledge), difficulty sustaining behavioural routines (TDF: behavioural regulation domain), and logistical challenges in accessing services (TDF: environmental context and resources domain). Providers and leaders emphasized poor communication across settings (CFIR: networks and communication construct), limited follow-up planning (CFIR: planning construct), and lack of sustainable funding models (CFIR: available resources construct). Enablers included strong social support (TDF: social influences domain), expanded roles for nurse practitioners and pharmacists (TDF: social/professional role and identity domain), and openness to virtual follow-up models (CFIR: adaptability construct). Suggested solutions include structured discharge education, interdisciplinary collaboration, standardized follow-up systems, and fostering a culture supportive of implementation. Conclusions: Applying behavioural and implementation frameworks identified multilevel barriers and enablers to post-STEMI follow-up. Actionable strategies, such as structured education, interdisciplinary care, and expanded nonphysician roles, could strengthen secondary prevention and improve outcomes.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".