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Record W4387883492 · doi:10.2196/50212

Teaching Basic Surgical Skills Using a More Frugal, Near-Peer, and Environmentally Sustainable Way: Mixed Methods Study

2023· article· en· W4387883492 on OpenAlexvenueno aff
Ben Smith, Christopher Paton, Prashanth Ramaraj

Bibliographic record

VenueJMIR Perioperative Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
FundersImperial College London
KeywordsLogbookMedicineEvisceration (ophthalmology)Medical education

Abstract

fetched live from OpenAlex

BACKGROUND: The Royal College of Surgeons Basic Surgical Skills (BSS) course is ubiquitous among UK surgical trainees but is geographically limited and costly. The COVID-19 pandemic has reduced training quality. Surveys illustrate reduced logbook completion and increased trainee attrition. Local, peer-led teaching has been shown to be effective at increasing confidence in surgical skills in a cost-effective manner. Qualitative data on trainee well-being, recruitment, and retention are lacking. OBJECTIVE: This study aims to evaluate the impact of a novel program of weekly, lunchtime BSS sessions on both quantitative and qualitative factors. METHODS: A weekly, lunchtime BSS course was designed to achieve the outcomes of the Royal College of Surgeons BSS course over a 16-week period overlapping with 1 foundation doctor rotation. All health care workers at the study center were eligible to participate. The study was advertised via the weekly, trust-wide information email. Course sessions included knot tying, suturing, abscess incision and drainage, fracture fixation with application of plaster of Paris, joint aspirations and reductions, abdominal wall closure, and basic laparoscopic skills. The hospital canteen sourced unwanted pig skin from the local butcher for suturing sessions and pork belly for abscess and abdominal wall closure sessions. Out-of-date surgical equipment was used. This concurrent, nested, mixed methods study involved descriptive analysis of perceived improvement scores in each surgical skill before and after each session, over 4 iterations of the course (May 2021 to August 2022). After the sessions, students completed a voluntary web-based feedback form scoring presession and postsession confidence levels on a 5-point Likert scale. Qualitative thematic analysis of voluntary semistructured student interview transcripts was also performed to understand the impact of a free-to-attend, local, weekly, near-peer teaching course on perceived well-being, quality of training, and interest in a surgical career. Students consented to the use of feedback and interview data for this study. Ethics approval was requested but deemed not necessary by the study center's ethics committee. RESULTS: =5.3117) across all surgical skills over 4 iterations. Among the 7 semistructured interviews, 100% (7/7) of the participants reported improved perceived well-being, value added to training, and positivity toward near-peer teaching and 71% (5/7) preferred local weekly teaching. Interest in a surgical career was unchanged. CONCLUSIONS: This course was feasible around clinical workloads, resourced locally at next to no cost, environmentally sustainable, and free to attend. The course offered junior doctors not only a weekly opportunity to learn but also to teach. Peer-led, decentralized surgical education increases confidence and has a positive effect on perceptions about well-being and training. We hope to disseminate this course, leading to reproduction in other centers, refinement, and wide implementation.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.289
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.037
GPT teacher head0.424
Teacher spread0.386 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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