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789 Best of both worlds: a hybrid simulation-based junior trainee mock code curriculum with complementary asynchronous e-learning modules

2022· article· en· W4292120839 on OpenAlexaff
Sabrina Lue Tam, Jabeen Fayyaz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsCurriculumFacilitatorMedical educationComputer sciencePDCAStakeholderMedicineQuality managementPsychologyEngineeringPedagogyManagement system

Abstract

fetched live from OpenAlex

Aims Managing pediatric emergencies is a core competency for medical learners, however clinical exposure varies widely during training. Thus, the Hospital for Sick Children established a standardized simulation-based Junior Mock Code (JMC) curriculum (figure 1) for first- and second-year residents. However, trainee and facilitator feedback identified persistent learning gaps, highlighting core pediatric emergencies that were missing from the original curriculum. We aimed to develop a hybrid simulation-based curriculum with complementary interactive asynchronous learning modules (figure 2) to better bridge these gaps. Methods Using the Kern Curriculum Development Model,1,2 we identified common clinical performance weaknesses, knowledge gaps, and low-frequency clinical exposures for junior trainees. We then prioritized the topics to be included in the new curriculum and established its goals and objectives: to provide supplement clinical exposure to core pediatric emergencies, including critical but low-frequency presentations (pediatric trauma and neonatal shock), through simulation-based education and complementary interactive asynchronous learning modules using the AffinityLearning™ platform. We launched our pilot in January 2022. Using Quality Improvement methodology including Plan, Do, Study, Act (PDSA) cycles, we are conducting multiple iterations of feedback collection from learners and facilitators and subsequent curriculum revisions. Results Our first PDSA cycle was gathering stakeholder feedback from a departmental curriculum presentation prior to launch. Pediatric Emergency Medicine faculty and trainees overall responded very positively to the content changes from the previous curriculum and further feedback on logistics, academic potential, and networking opportunities was provided. These logistic suggestions were implemented in designing a curriculum dissemination protocol with administrative support staff. Our second cycle was a peer review of the module content and technological functionality by pediatric emergency medicine faculty and senior trainees, and feedback from this peer review was integrated prior to curriculum launch. Our current cycle started with curriculum implementation and focuses on learner reactions (Kirkpatrick level 1) and learner knowledge retention (Kirkpatrick level 2) outcomes based on feedback surveys for the asynchronous learning modules and aggregate learner performance reports on self-assessment activities embedded in each module. Conclusion Future steps for this curriculum will include assessing trainee performance/competency scores during simulation sessions and correlating these with their performance on the preceding complementary asynchronous e-learning modules (Kirkpatrick level 3 learning outcomes). We will also continuously expand the library of e-learning modules to further bridge learning gaps for junior trainees, empowering them to reflect on their knowledge and areas for improvement as self-directed adult learners. References Thomas PA, et al. Curriculum Development for Medical Education: A Six-Step Approach. Barsuk JH, et al. Developing a Simulation-Based Mastery Learning Curriculum. Kurt S. Kirkpatrick Model: Four Levels of Learning Evaluation.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.076
GPT teacher head0.428
Teacher spread0.352 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2022
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