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Record W7108339297 · doi:10.2196/67332

Global Learner Feedback on Continuing Medical Education–Accredited e-Learning Modules in Pediatric Endocrinology and Diabetes: Cross-Sectional Study

2025· article· en· W7108339297 on OpenAlexvenueno aff

Bibliographic record

VenueJMIR Medical Education · 2025
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersEuropean CommissionAlexion PharmaceuticalsInternational Society for Pediatric and Adolescent DiabetesEli Lilly and Company
KeywordsPediatric endocrinologyContinuing medical educationPromotion (chess)Equity (law)Continuing educationMEDLINE

Abstract

fetched live from OpenAlex

Background: The European Society for Paediatric Endocrinology (ESPE) e-Learning wesite is a free, globally accessible online resource to enhance learning in pediatric endocrinology and pediatric diabetes. The content is created by world-leading experts in pediatric endocrinology and pediatric diabetes and is closely aligned with published international consensus guidelines. In August 2022, 30 hours of e-learning courses received accreditation from the European Accreditation Council for Continuing Medical Education (CME). These CME courses cover three categories: (1) pediatric endocrinology, (2) pediatric diabetes, and (3) pediatric endocrinology in resource-limited settings. Objective: This study aimed to assess learners' demographics and feedback from mandatory surveys after completion of CME e-learning courses and to identify areas for improvement. Methods: The ESPE e-learning committee created a mandatory survey for each CME e-learning module. The survey includes baseline demographics and feedback on the quality of the learning content, assessed using a 5-point Likert scale. Data were extracted from the start of the CME modules in August 2022 until September 2025. Results: A total of 567 surveys were completed: 286 (50.4%) in the category pediatric endocrinology, 225 (39.7%) in the category pediatric diabetes based on the International Society for Pediatric and Adolescent Diabetes guidelines, and 56 (9.9%) in the category pediatric endocrinology in resource-limited settings. There was global participation, with most learners practicing in Europe (n=333, 59%), followed by Asia (n=124, 22%), Africa (n=53, 9%), the Americas (North America, n=45, 8%; and South America, n=11, 2%), and Oceania (n=1, 0%). Most of the users indicated that they were medical experts (n=210, 37%), followed by fellows or residents (n=223, 39%), and medical students and nurses (n=29, 5% and n=32, 6%, respectively); overall, 10% (n=56) of learners practice in resource-limited countries. Overall, the learning content was well received for all modules regarding accessibility, organization, level of interest, improvement of learners' clinical practice, appropriateness of content, and provision of feedback (median Likert score 4, IQR 4-5). Learners' free-text feedback identified some areas for improvement, including reducing text-heavy content and providing more graphical content and more interactive case reports. Most learners' free-text feedback consisted of encouraging and thankful comments. Conclusions: The ESPE CME-accredited e-learning modules are well received, providing globally free CME education in pediatric endocrinology and pediatric diabetes. These findings support the continued development and promotion of open-access CME platforms, with the aim of improving global equity in specialist medical education and focusing on educational impact.

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)0.013
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.366
Teacher spread0.358 · 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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Citations1
Published2025
Admission routes1
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