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Record W2978986145 · doi:10.1097/sla.0000000000003573

Mental Skills in Surgery

2019· article· en· W2978986145 on OpenAlexaffabout
Siobhan Deshauer, Sydney McQueen, Melanie Hammond Mobilio, Dorotea Mutabdzic, Carol‐Anne Moulton

Bibliographic record

VenueAnnals of Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineEliteMedical educationMental healthConstructivist grounded theoryApplied psychologyGrounded theoryPsychologyPsychiatryQualitative research

Abstract

fetched live from OpenAlex

OBJECTIVE: The present study investigated the role of mental skills in surgery through the unique lens of current surgeons who had previously served as Olympic athletes, elite musicians, or expert military personnel. BACKGROUND: Recent work has demonstrated great potential for mental skills training in surgery. However, as a field, we lag far behind other high-performance domains that explicitly train and practice mental skills to promote optimal performance. Surgery stands to benefit from this work. First, there is a need to identify which mental skills might be most useful in surgery and how they might be best employed. METHODS: Using a constructivist grounded theory approach, semi-structured interviews were conducted with 17 surgeons across the United States and Canada who had previously performed at an elite level in sport, music, or the military. RESULTS: Mental skills were used both to optimize performance in the moment and longitudinally. In the moment, skills were used proactively to enter an ideal performance state, and responsively to address unwanted thoughts or emotions to re-enter an acceptable performance zone. Longitudinally, participants used skills to build expertise and maintain wellness. CONCLUSIONS: Establishing a taxonomy for mental skills in surgery may help in the development of robust mental skills training programs to promote optimal surgeon wellness and performance.

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.256
GPT teacher head0.375
Teacher spread0.119 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2019
Admission routes2
Has abstractyes

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