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Record W3199706563 · doi:10.1177/23821205211037756

Development, Implementation, and Initial Participant Feedback of an Online Anatomy and Radiology Contouring Bootcamp in Radiation Oncology

2021· article· en· W3199706563 on OpenAlexaff
Paige Eansor, Madeleine E. Norris, Leah D’Souza, Glenn Bauman, Zahra Kassam, Eric Leung, Anthony C. Nichols, Manas Sharma, Keng Yeow Tay, Vikram Velker, Andrew Warner, Katherine E. Willmore, David A. Palma, Nicole Campbell

Bibliographic record

VenueJournal of Medical Education and Curricular Development · 2021
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsPrincess Margaret Cancer CentreSt Joseph's Health CareLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsContouringMedicineMedical physicsRadiation oncologyMedical educationRadiologyCurriculumRadiation therapyComputer sciencePsychologyPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: The Anatomy and Radiology Contouring (ARC) Bootcamp was a face-to-face (F2F) course designed to ensure radiation oncology residents were equipped with the knowledge and skillset to use radiation therapy techniques properly. The ARC Bootcamp was proven to be a useful educational intervention for improving learners' knowledge of anatomy and radiology and contouring ability. An online version of the course was created to increase accessibility to the ARC Bootcamp and provide a flexible, self-paced learning environment. This study aimed to describe the instructional design model used to create the online offering and report participants' motivation to enroll in the course and the online ARC Bootcamp's strengths and improvement areas. METHODS: The creation of the online course followed the analysis, design, development, implementation, and evaluation (ADDIE) framework. The course was structured in a linear progression of locked modules consisting of radiology and contouring lectures, anatomy labs, and integrated evaluations. RESULTS: The online course launched on the platform Teachable in November 2019, and by January 2021, 140 participants had enrolled in the course, with 27 participants completing all course components. The course had broad geographic participation with learners from 19 different countries. Of the participants enrolled, 34% were female, and most were radiation oncology residents (56%), followed by other programs (24%), such as medical physics residents or medical students. The primary motivator for participants to enroll was to improve their subject knowledge/skill (44%). The most common strength identified by participants was the course's quality (41%), and the most common improvement area was to incorporate more course content (41%). CONCLUSIONS: The creation of the online ARC Bootcamp using the ADDIE framework was feasible. The course is accessible to diverse geographic regions and programs and provides a flexible learning environment; however, the course completion rate was low. Participants' feedback regarding their experiences will inform future offerings of the online course.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.367
Teacher spread0.341 · 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 designOther design
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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