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Record W4309726550 · doi:10.1002/aet2.10818

The next generation of researchers: <scp>One‐year</scp> outcome data from the <scp>SAEM Advanced Research Methodology Evaluation and Design in Medical Education</scp> ( <scp>ARMED MedEd</scp> ) program

2022· article· en· W4309726550 on OpenAlexaff
Michael Gottlieb, Teresa M. Chan, Stefanie S. Sebok‐Syer, Sara Krzyzaniak, Nicole M. Dubosh, Sally A. Santen, Holly Caretta‐Weyer, Lalena M. Yarris, Wendy C. Coates

Bibliographic record

VenueAEM Education and Training · 2022
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScholarshipMedical educationInclusion (mineral)Program evaluationPsychologyInterquartile rangeMedicinePolitical science

Abstract

fetched live from OpenAlex

Introduction: As the field of medical education evolves, there is a need to increase the quality of education scholarship and develop a cadre of research scholars; however, clinician educators (CEs) considering this career transition have limited formal training in education research methodology to heed this call. Therefore, a program that provides more advanced training in education scholarship for CEs without the financial and resource barriers of fellowships and masters programs is needed. Methods: The SAEM Advanced Research Methodology Evaluation and Design in Medical Education (ARMED MedEd) program is a longitudinal program for the beyond-beginner CE, seeking advanced training in education research. The program was created using a comprehensive needs assessment and included longitudinal training; small-group projects; dedicated project mentors; and integrated diversity, equity, and inclusion initiatives. Program participants applied for a grant upon program completion. Results: Twenty-one participants completed the course with 100% completing the baseline survey and 67% (14/21) completing the end-of-program survey. Participants reported improved perception of knowledge across all of the topics with a medium to large effect size, ranging from 0.40 to 0.62. When asked about impact on their network of potential collaborators, participants reported a median of 7 (interquartile range [IQR] 5-8) out of 9. When asked about the impact on their community of practice, participants reported a median of 7 (IQR 5-7) out of 9. When asked about the impact on their professional identity, participants reported a median of 7 (IQR 4-9) out of 9. Participants also reported an increase in both the quantity (mean of 2 ± 1 new mentors) and the quality (median score 7 [IQR 5-8] out of 9) of new research mentorship as a result of the program. Open-ended feedback was generally positive, with 100% reporting they would advise others to take this program. Conclusions: The SAEM ARMED MedEd program represents a proof of concept for an advanced education research program seeking to fill the research training gap for the beyond-beginner Clinician educators.

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)0.036
metaresearch head score (Gemma)0.154
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0360.154
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.717
GPT teacher head0.569
Teacher spread0.148 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2022
Admission routes1
Has abstractyes

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