Adult Congenital Heart Disease as a Career? Examining Encouraging and Deterring Factors in the Global ACHD Survey
Bibliographic record
Abstract
BACKGROUND: There is a global mismatch between the number of adults with congenital heart disease and adult congenital heart disease (ACHD) cardiologists, potentially impacting patient outcomes. This survey explores factors influencing trainees and cardiologists' career choices in ACHD. METHODS: An online, anonymous global survey on demographics, ACHD as a career choice, encouraging and deterring factors assessed via a 5-point Likert scale and open-ended questions on recommending ACHD was distributed through targeted emails and social media. Responses meeting a 75% completion criterion were analyzed using descriptive statistics and thematic analysis. RESULTS: Among 811 included responses, most were from women (53.5%), aged 31 to 40 years (48.3%), White individuals (43.3%), non-Hispanic individuals (85%), from outside the ACHD field (79.0%), and from North America (42.7%). Only 30.9% of non-ACHD physicians considered specializing in ACHD despite 78.9% recommending it. Encouraging factors included awareness of ACHD significance, mentor influence, and clinical exposure. Key deterrents were inadequate financial incentives, limited job opportunities, and lengthy training. Regional variations showed that longer training duration and lack of compensation incentives were the main deterrents in North America. Suggested solutions included structured training programs, salary improvements, and early ACHD exposure during medical education. CONCLUSIONS: Less than a third of respondents considered ACHD as a career largely due to inadequate financial incentives, limited job opportunities, and long training duration. Addressing these barriers could enhance recruitment and help meet the growing demands of the ACHD population.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".