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Record W3006884066 · doi:10.1093/jcag/gwz047.137

A138 CONCEPTUALIZING ENTRUSTMENT IN ENDOSCOPIC TRAINING

2020· article· en· W3006884066 on OpenAlexaffabout
Thurarshen Jeyalingam, Shiphra Ginsburg, Graham A. McCreath, Catharine M. Walsh

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsSickKids FoundationHospital for Sick ChildrenThe Wilson CentreUniversity of Toronto
Fundersnot available
KeywordsTrainerGrounded theoryConstructivist grounded theoryMedical educationMedicinePsychologyQualitative researchLikert scaleConstructivist teaching methodsNursingPedagogyTeaching methodComputer scienceSociology

Abstract

fetched live from OpenAlex

Abstract Background Competency-based medical education (CBME), an educational paradigm that prioritizes development of measurable skills over time in training, is currently being instituted across North American residency training programs. A fundamental goal of CBME is entrustment – the process whereby supervisors come to trust trainees to perform specific tasks without supervision. How entrustment decisions are made with respect to endoscopic training has not been elucidated. Aims We aimed to: (1) identify the factors trainers consider in making endoscopic entrustment decisions and (2) characterize this entrustment decision-making process. Methods A qualitative, interview-based study was conducted using a constructivist grounded theory approach. A purposive sample of endoscopic trainers from across North America were recruited with representation from adult and pediatric gastroenterology, general surgery, and family medicine. Consenting trainers undertook audio-recorded, semi-structured interviews designed to elicit how they make endoscopic entrustment decisions and the factors they consider in making these decisions. Interview transcripts were analyzed using constant comparison, and themes were identified iteratively, working toward an explanatory framework that highlighted relationships among themes. Recruitment continued alongside analysis until theoretical saturation, determined as the point at which no new insights arose from the data. Results Twenty-three trainer interviews were conducted, comprising 8 (34.8%) with adult gastroenterologists, 7 (30.4%) with general surgeons, 6 (26.1%) with pediatric gastroenterologists, and 2 (8.7%) with family physicians. Of those interviewed, 10 (43.5%) practiced in Canada and 13 (56.5%) practiced in the United States. Interviewees conceptualized entrustment as a continuum rather than a binary (yes/no) decision. Entrustment decision-making was found to be a complex process, involving factors related to the: (1) trainee (insight into their own abilities, technical skills, nontechnical skills); (2) trainer (perceived self-competence, disposition, prioritization of trainee learning relative to competing demands); (3) trainer-trainee relationship (duration of exposure); (4) patient (acuity, comorbidity, comfort); (5) procedure (complexity); and (6) environment (time constraints, equipment, presence of anesthesiologist). These factors directly and indirectly contributed to the themes of trainee readiness, trainer comfort, and patient safety, which collectively predicted endoscopic entrustment decisions. Conclusions Entrustment in endoscopic training is a complex process incorporating multiple factors. Clarification of this process and identification of predictive factors informs the development of endoscopic assessment tools and curricula uniquely suited to CBME. Funding Agencies CAG

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.002
Science and technology studies0.0070.032
Scholarly communication0.0080.011
Open science0.0030.010
Research integrity0.0020.003
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 head0.025
GPT teacher head0.277
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2020
Admission routes2
Has abstractyes

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