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Record W4206615102 · doi:10.1097/hco.0000000000000945

The anatomy of enjoyment: the flow experience and cardiac surgery

2021· article· en· W4206615102 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Opinion in Cardiology · 2021
Typearticle
Languageen
FieldPsychology
TopicFlow Experience in Various Fields
Canadian institutionsQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsCardiac surgeryFlow (mathematics)Component (thermodynamics)Key (lock)Blood flow

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.391
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it