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 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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".