The anatomy of enjoyment: the flow experience and cardiac surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: In a time of record levels of physician burnout coupled with a global pandemic, protecting physician wellness is critical. The experience of cognitive flow has been found to enhance both wellness and performance. Although flow has been vastly explored in other fields including elite sport, it has not been deeply investigated or applied in cardiac surgery. Here we discuss flow and flow-promoting techniques employed in other fields that may be beneficial within cardiac surgery. RECENT FINDINGS: Flow is a prevalent experience among surgeons, amplified during operations. Possible strategies to cultivate flow may be separated into individual skills training, such as mindfulness practice and stress management, institutional changes, such as ensuring adequate resources and protected spaces, and strategies targeting the intersectionality of individuals and systems, such as how workplace culture shapes an individual's experience. These techniques may be applicable within cardiac surgery, especially in training. SUMMARY: Flow has been identified as a key component of a happy and meaningful life, and a potential protector against burnout. Harnessing the benefits of flow may help promote flourishing, particularly in demanding fields, such as cardiac surgery.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".