163: Evaluation of an eLearning Module for an Effective Well-Baby Visit Using the Rourke Baby Record
Bibliographic record
Abstract
With the emerging evidence that early childhood development and experiences affect not only learning, but also lifelong physical, mental and emotional health, it is important that medical students learn to conduct effective comprehensive well-baby/well-child visits. The Rourke Baby Record (RBR) is a Canadian evidence-based validated system for well-baby/well-child care from one week to five years of age. E-Learning has gained popularity as a new approach to medical education. To assess medical student knowledge of well-baby care and the RBR before and after completing an eLearning module of a six month well-baby visit of a healthy infant. To obtain student feedback regarding the module content, clinical applicability, and web delivery. Module development began with a medical student summer research award, enabling a student vision for the module format, and involved an instructional design specialist in addition to family physician and paediatric faculty. Participants were medical students from two stages of training: preclinical second year (Y2) and third year clerkship (Y3). The data was analyzed for: 1) knowledge gains using a pre/post test of 10 MCQs; 2) module satisfaction using a questionnaire. Most survey questions used a 5-point Likert scale with results grouped into three categories: agree (strongly agree or agree), neutral, or disagree (disagree or strongly disagree). The 121 student participants comprised 96% of the two medical school classes. Both Y2 and Y3 students had a significant increase in their post test scores (P<0.001). For most MCQs, the Y3 students scored slightly higher. Module completion time averaged 90 minutes (range 20 min to 3 h). The extensive involvement of a medical student and an instructional design specialist resulted in a creative, visually appealing module. On average, 90.9% of students answered satisfaction survey questions positively. The majority of students indicated the module should be part of the curriculum, significantly more by Y3 students (78.8% of Y2; 98.0% of Y3 students, P=0.030). More Y3 than Y2 students felt the module enhanced their knowledge (P=0.016) and provided learning applicable to patient care (P=0.022). Suggestions for improvement were largely focused on web delivery and test questions. This eLearning module was an effective tool to teach medical students how to conduct a six month well-baby visit. The module was popular with students; suggestions for improvement were minor. A second module of an 18 month well-child visit is currently under development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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".