Effect of Psychosocial and Vocational Interventions on Return-to-Work Rates Post–Acute Myocardial Infarction
Bibliographic record
Abstract
PURPOSE: To examine whether the effects of psychosocial and vocational interventions delivered in the first 3 mo post-acute myocardial infarction (AMI) are effective for improving work outcomes compared with other interventions. METHODS: A search was completed for English language publications up to March 2016 across 4 electronic databases and gray literature. Inclusion criteria were (1) psychosocial and/or vocational interventions; (2) adults 18 years or older with an AMI who were within the first 3 mo post-AMI; (3) randomized or clinically controlled trials; and (4) reporting of at least 1 return-to-work (RTW) outcome: including return to paid/unpaid employment, either full-time or part-time, to the previous job role or on modified duties. Studies were independently screened by 2 reviewers and graded using the Cochrane Collaboration tool for assessing risk of bias. A narrative synthesis and meta-analysis of the included studies was undertaken. RESULTS: Eighteen studies of varying quality were analyzed. Individually delivered psychosocial and vocational interventions may improve work rates at 3 mo (relative risk = 1.17; P = .05) when compared with usual care but there was no difference at 6 or 12 mo. People receiving group or individual psychological/vocational counselling returned to work 6.11 d sooner than those who received usual care (95% CI, -6.95 to -5.26; P < .001). CONCLUSIONS: Although psychosocial and vocational interventions show promise in people within 3 mo of AMI, compelling evidence is still limited. There is a need for more detailed, high-quality studies with valid comparison groups and adequate follow-up.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".