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Record W4399418361 · doi:10.1055/s-0044-1786982

Gathering Dust—Resistance to Simulator-based Deliberate Practice in Microsurgical Training

2024· article· en· W4399418361 on OpenAlexafffundabout
Claire Temple‐Oberle, A. Robertson Harrop, Carmen Webb, Susan Somerville

Bibliographic record

VenueJournal of Reconstructive Microsurgery Open · 2024
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of Calgary
FundersRoyal College of Physicians and Surgeons of CanadaUniversity of Dundee
KeywordsTraining (meteorology)Resistance (ecology)SimulationSimulation trainingComputer scienceMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract Background Despite unrestricted access to a simulated microsurgery model, learners have not consistently self-regulated their learning by completing practice. This paper explores the lived experience of learners regarding how practice is perceived and why it is resisted. Methods A qualitative study was conducted, including recorded and transcribed focus groups and semistructured interviews. First and second pass coding was conducted by one reviewer, with feedback from another. Transcripts were analyzed with a constant comparative approach customary to thematic analysis. Theory was engaged to help explain and support the findings. The study was undertaken at the University of Calgary plastic surgery residency training program in Calgary, Alberta, Canada, involving 15 informants (9 residents and 6 surgeons). Results Four themes emerged: (1) barriers to practice, (2) motivation to practice, (3) owning learning/solutioning, and (4) expectations of practice. Competing priorities and time constraints were barriers. Motivation to practice ranged from extrinsic (gaining access to the next course) to intrinsic (providing optimal patient care). Learners described a range of ownership of learning and depth of effort at solutioning of practice opportunities. Learners expressed high expectations around model fidelity, ease of setup, and feedback. Learners self-regulating their learning, with surgeons acculturating practice at work, can overcome some barriers. As per self-determination theory (SDT), learners need explicit linkage to how the task aligns with their goals. Assessment may be required to motivate learners. In respect of adult learning theory, homework needs to be allocated by a respected trainer. Modeling simulation practice may encourage adult learners. Finally, the tenets of deliberate practice (DP) need to be explained in order that learners can optimize their practice time. Conclusion Microsurgical simulation practice is valued but barriers exist that invite resolution. Assisting residents to overcome barriers, maintain motivation, take ownership, and assimilate DP will help improve their microsurgery practice.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.357
Teacher spread0.309 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes3
Has abstractyes

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