Development and Assessment of an E-learning Course on Pediatric Cardiology Basics
Bibliographic record
Abstract
BACKGROUND: Early detection of congenital heart disease is a worldwide problem. This is more critical in developing countries, where shortage of professional specialists and structural health care problems are a constant. E-learning has the potential to improve capacity, by overcoming distance barriers and by its ability to adapt to the reduced time of health professionals. OBJECTIVE: The study aimed to develop an e-learning pediatric cardiology basics course and evaluate its pedagogical impact and user satisfaction. METHODS: The sample consisted of 62 health professionals, including doctors, nurses, and medical students, from 20 hospitals linked via a telemedicine network in Northeast Brazil. The course was developed using Moodle (Modular Object Oriented Dynamic Learning Environment; Moodle Pty Ltd, Perth, Australia) and contents adapted from a book on this topic. Pedagogical impact evaluation used a pre and posttest approach. User satisfaction was evaluated using Wang's questionnaire. RESULTS: Pedagogical impact results revealed differences in knowledge assessment before and after the course (Z=-4.788; P<.001). Questionnaire results indicated high satisfaction values (Mean=87%; SD=12%; minimum=67%; maximum=100%). Course adherence was high (79%); however, the withdrawal exhibited a value of 39%, with the highest rate in the early chapters. Knowledge gain revealed significant differences according to the profession (X22=8.6; P=.01) and specialty (X22=8.4; P=.04). Time dedication to the course was significantly different between specialties (X22=8.2; P=.04). CONCLUSIONS: The main contributions of this study are the creation of an asynchronous e-learning course on Moodle and the evaluation of its impact, confirming that e-learning is a viable tool to improve training in neonatal congenital heart diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".