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Record W4365790862 · doi:10.2196/42060

Intervention for Intraoperative Teaching in Anesthesiology Using Weekly Keyword Program: Development and Usability Study

2023· article· en· W4365790862 on OpenAlexvenueno aff
George Tewfik, Rotem Naftalovich, Carlos Rodriguez-Aponte, Bishoy Ezzat

Bibliographic record

VenueInteractive Journal of Medical Research · 2023
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumAnesthesiologyMedical educationWorksheetUsabilitySet (abstract data type)MedicineStandardizationPsychologyComputer scienceMathematics educationAnesthesiaPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Learning in the operating room (OR) for residents in anesthesiology is difficult but essential for successful resident education. Numerous approaches have been attempted in the past to varying degrees of success, with efficacy often judged afterward using surveys distributed to participants. The OR presents a particularly complex set of challenges for academic faculty due to the pressures required by concurrent patient care, production pressures, and a noisy environment. Often, educational reviews in ORs are personnel specific, and instruction may or may not take place in this setting, as it is left to the discretion of the parties without regular direction. OBJECTIVE: This study aims to determine if a structured intraoperative keyword training program could be used to implement a curriculum to improve teaching in the OR and to facilitate impactful discussion between residents and faculty. A structured curriculum was chosen to allow for the standardization of the educational material to be studied and reviewed by faculty and trainees. Given the reality that educational reviews in the OR tend to be personnel specific and are often focused on the clinical cases of the day, this initiative sought to increase both the time and efficiency of learning interactions between learners and teachers in the stressful environment of the OR. METHODS: The American Board of Anesthesiology keywords from the Open Anesthesia website were used to construct a weekly intraoperative didactic curriculum, which was distributed by email to all residents and faculty. A weekly worksheet from this curriculum included 5 keywords with associated questions for discussion. The residents and faculty were instructed to complete these questions on a weekly basis. After 2 years, an electronic survey was distributed to the residents to evaluate the efficacy of the keyword program. RESULTS: A total of 19 teaching descriptors were polled for participants prior to and following the use of the intraoperative keyword program to assess the efficacy of the structured curriculum. The survey results showed no improvement in intraoperative teaching based on respondent perception, despite a slight improvement in teaching time, though this was statistically insignificant. The respondents reported some favorable aspects of the program, including the use of a set curriculum, suggesting that greater structure may be beneficial to facilitate more effective intraoperative teaching in anesthesiology. CONCLUSIONS: Although learning is difficult in the OR for residents, the use of a formalized didactic curriculum, centered on daily keywords, does not appear to be a useful solution for residents and faculty. Further efforts are required to improve intraoperative teaching, which is well known to be a difficult endeavor for both teachers and trainees. A structured curriculum may be used to augment other educational modalities to improve the overall intraoperative teaching for anesthesia residents.

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)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.247
GPT teacher head0.566
Teacher spread0.319 · 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 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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