P.111 The surgeon experience of flow
Bibliographic record
Abstract
Background: Cognitive flow has been linked with enhanced performance, career satisfaction, and decreased burnout. However, while elite sport has long trained athletes to enter flow states, the concept has not been adopted strongly in healthcare. Flow has primarily been explored from a unidimensional (cognitive) perspective. The present study sought to understand the experience of flow among surgeons through a multidimensional lens. Methods: Using a constructivist grounded theory methodology, semi-structured interviews were conducted with 19 staff surgeons at the University of Toronto, purposively sampled. Data were coded and analyzed iteratively by three researchers until theoretical saturation was achieved. Results: Although many surgeons had not previously heard of cognitive flow, the phenomenon deeply resonated with most. Participants described different physical, cognitive, emotional, sociocultural, and environmental components that interacted to shape the subjective experience of flow: “I think that there are many different facets of [flow] that don’t always come together all at the same time, you may feel different parts of it… depending on what the kind of case is, who your help is, if you recently had a complication.” (P4) Conclusions: Understanding flow in clinical practice may lead to new avenues for enhancing career satisfaction and promoting physician wellness.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".