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Record W4307047761 · doi:10.12688/mep.19025.1

Surgical residents’ approach to training: are elements of deliberate practice observed?

2022· article· en· W4307047761 on OpenAlexfundno aff
Kendra Nelson Ferguson, Josée Paradis

Bibliographic record

VenueMedEdPublish · 2022
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersAcademic Medical Organization of Southwestern Ontario
KeywordsContext (archaeology)ExcellenceThematic analysisPsychologyQualitative researchSociologyPolitical scienceSocial scienceGeography

Abstract

fetched live from OpenAlex

<ns4:p> <ns4:bold>Background:</ns4:bold> Research in the area of deliberate practice has consistently shown that intense, concentrated, goal-oriented practice in a focused domain, such as medicine, can improve both skill development and performance to attain a progressively higher standard of excellence. In theory, utilizing deliberate practice in a medical context could result in improved surgical training and in turn better patient outcomes. Therefore, the purpose of this study was to gain a better understanding of how surgical residents approach their training from the perspective of the surgical residents themselves and to explore if elements of deliberate practice are observed. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> Eight surgical trainees participated in one of two focus groups depending on their training level (five junior residents; three senior residents). With the exploratory nature of this research, a focus group methodology was utilized. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> By employing both deductive and inductive thematic analysis techniques, three themes were extracted from the data: learning resources and strategies, role of a junior/senior, and approaching weaknesses. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> Although elements of deliberate practice were discussed, higher functioning is necessary to achieve performance excellence, leading to improved patient outcomes. </ns4:p>

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)0.003
metaresearch head score (Gemma)0.006
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.352
Teacher spread0.252 · 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 designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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