Factors influencing return to work among cardiovascular disease patients: a scoping review
Bibliographic record
Abstract
Abstract Objectives Cardiovascular diseases (CVDs) are a leading cause of morbidity worldwide. Returning to work (RTW) after CVD enhances individual well-being and societal productivity. This review maps the existing literature, identifies key factors influencing RTW, and highlights evidence gaps to guide future research. Methods The review followed PRISMA checklists and Cochrane-GESI guidelines, including studies published from January 2019 to February 2025 on RTW after CVD. The research was conducted across four databases and five journals, using the terms “RTW,” “stroke,” and “CVD." Results After the screening process, sixty-nine studies met the eligibility criteria, focusing on working-age patients affected by acute CVDs (mainly TIA and minorly MI and other). Among these, forty-one articles explored key factors influencing RTW, including age, sex, smoking, anxiety, depression, family support, comorbidities, job type, and recurrence. Nineteen studies examined patient perspectives, highlighting the need for constant support and clear communication between employees, employers, caregivers, and rehabilitation professionals, whose perspectives were reviewed in three articles. Lastly, eighteen articles analyzed strategies aimed at optimizing RTW outcomes by analyzing various approaches for patient management (n = 11), and/or quantifying the patient's status before and after RTW by evaluating either QoL or functional status (n = 7). A clear consensus on which approach is the most effective is still lacking, aside from the recognized importance of a multidisciplinary intervention integrating physical and psychological factors along with workplace adjustments. Discussion Clear RTW after CVD guidelines lack. Future research should validate the most effective strategies and assessment tools, ensuring an optimized RTW pathway. Key messages • Guidelines on RTW after cardiovascular events are lacking. • A multidisciplinary approach and work adjustments have been found crucial for a better return to work.
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 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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".