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Record W3119577425 · doi:10.1111/medu.14449

Before the scalpel: Exploring surgical residents' preoperative preparatory strategies

2021· article· en· W3119577425 on OpenAlexaff
Danielle C. Cadieux, Anuradha Mishra, Mark Goldszmidt

Bibliographic record

VenueMedical Education · 2021
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsDalhousie UniversityWestern University
Fundersnot available
KeywordsMedicineSurgical proceduresGeneral surgeryPreoperative careMEDLINEMedical educationSurgeryPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: This study sought to increase understanding of preoperative preparatory strategies utilised by senior surgical residents and identify how social and material forces come together to shape practice. SUMMARY/BACKGROUND DATA: Preoperative preparation can play a powerful role in operative learning. Residents rarely receive guidance, feedback, or explicit expectations on how to prepare for the OR. Understanding current practice and how to support preoperative preparation represents an important gap in our efforts to improve surgical training. METHODS: Constructivist grounded theory with sensitizing concepts from sociomateriality guided data collection and analysis. Fifteen senior surgical residents from a range of surgical disciplines were purposefully sampled and participated in an in-depth individual interview. Two return-of-finding focus groups followed with seven residents. Rigor was enhanced through constant comparison, theoretical sampling, pursuit of discrepant data, and investigator triangulation. RESULTS: Residents utilised a range of strategies addressing four areas of focus: develop technical skills, improve procedural knowledge, enhance patient-specificity, and know surgical preferences. However, residents also described receiving limited guidance on what it means to 'be prepared' and experience significant challenges in achieving preparedness. A mix of social and material things that enabled or constrained preparatory efforts influenced individual strategies. These included rotation structure, relationships, the OR list, and time. CONCLUSIONS: Our findings offer possible solutions by elaborating on preparatory variability and considerations for residents, faculty, and programs to improve practice. As a first step, we suggest programs begin to engage in explicit dialogue and reflection with their residents, faculty, and residency program committees.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.041
GPT teacher head0.367
Teacher spread0.325 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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