Education as important predictor for successful employment in adults with congenital heart disease worldwide
Bibliographic record
Abstract
BACKGROUND: Conflicting results have been reported regarding employment status and work ability in adults with congenital heart disease (CHD). Since this is an important determinant for quality of life, we assessed this in a large international adult CHD cohort. METHODS: Data from 4028 adults with CHD (53% women) from 15 different countries were collected by a uniform survey in the cross-sectional APPROACH International Study. Predictors for employment and work limitations were studied using general linear mixed models. RESULTS: Median age was 32 years (IQR 25-42) and 94% of patients had at least a high school degree. Overall employment rate was 69%, but varied substantially among countries. Higher education (OR 1.99-3.69) and having a partner (OR 1.72) were associated with more employment; female sex (OR 0.66, worse NYHA functional class (OR 0.67-0.13), and a history of congestive heart failure (OR 0.74) were associated with less employment. Limitations at work were reported in 34% and were associated with female sex (OR 1.36), increasing age (OR 1.03 per year), more severe CHD (OR 1.31-2.10), and a history of congestive heart failure (OR 1.57) or mental disorders (OR 2.26). Only a university degree was associated with fewer limitations at work (OR 0.62). CONCLUSIONS: There are genuine differences in the impact of CHD on employment status in different countries. Although the majority of adult CHD patients are employed, limitations at work are common. Education appears to be the main predictor for successful employment and should therefore be encouraged in patients with CHD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".