EACVI survey on burnout amongst cardiac imaging specialists during the 2019 coronavirus disease pandemic
Bibliographic record
Abstract
AIMS: The European Association of Cardiovascular Imaging Scientific Initiatives Committee conducted a global survey to evaluate the impact of the 2019 coronavirus disease (COVID-19) pandemic on the mental well-being of cardiac imaging specialists. METHODS AND RESULTS: In a prospective international survey performed between 23 July 2021 and 31 August 2021, we assessed the mental well-being of cardiac imaging specialists ∼18 months into the COVID-19 pandemic. One-hundred-and-twenty-five cardiac imaging specialists from 34 countries responded to the survey. More than half described feeling anxious during the pandemic, 34% felt melancholic, 27% felt fearful, and 23% respondents felt lonely. A quarter of respondents had increased their alcohol intake and more than half reported difficulties in sleeping. Two-thirds of respondents described worsening features of burnout during the past 18 months, 44% considered quitting their job. One in twenty respondents had experienced suicidal ideation during the pandemic. Despite these important issues, the majority of participants (57%) reported having no access to any formal mental health support at work. CONCLUSION: The survey has highlighted important issues regarding the mental well-being of cardiac imaging specialists during the COVID-19 pandemic. This is a major issue in our sub-specialty, which requires urgent action and prioritization so that we can improve the mental health of cardiovascular imaging specialists.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| 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".