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Record W3107622944 · doi:10.1002/aet2.10553

LIMEs and LEMONs: Critically Examining the Effect of a Blog Post on Junior Faculty Learners

2020· article· en· W3107622944 on OpenAlexaboutno aff
Anne Messman, Robert R. Ehrman, Larry D. Gruppen

Bibliographic record

VenueAEM Education and Training · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
FundersSociety for Academic Emergency Medicine
KeywordsDemographicsActive listeningAccreditationTest (biology)Wilcoxon signed-rank testRandomized controlled trialMedical educationGraduate medical educationMedicinePsychologyFamily medicineCurriculumPedagogy

Abstract

fetched live from OpenAlex

INTRODUCTION: The usage of asynchronous resources such as blogs and podcasts is pervasive in academic medicine, despite little understanding of their actual effect on learner knowledge acquisition. This study sought to examine the objective effect of a blog post on knowledge acquisition and application among junior faculty in emergency medicine (EM) via randomized controlled study. METHODS: All accredited EM residency programs in the United States and Canada were contacted to identify assistant and associate program directors and medical education fellows for recruitment into this study. Upon enrollment, participants were randomized as to whether they received access to a supplemental blog post prior to listening to a podcast episode. After listening to the podcast episode, all participants completed an assessment that included a test of knowledge application and knowledge acquisition; demographic information was also obtained. RESULTS: Ultimately, 103 participants completed the study; the study closed for enrollment in July 2019. Data were nonnormally distributed and groups were compared using the Wilcoxon rank-sum test. There were no significant differences between the demographics of the two groups nor was there a significant difference in knowledge between the two groups. CONCLUSION: The addition of a supplementary blog post did not increase junior faculty knowledge of a podcast episode.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
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 head0.166
GPT teacher head0.431
Teacher spread0.265 · 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 designQualitative
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

Citations2
Published2020
Admission routes1
Has abstractyes

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