Impact of a web-based module on trainees’ ability to interpret neonatal cranial ultrasound
Bibliographic record
Abstract
BACKGROUND: Accurate interpretations of neonatal cranial ultrasound (CUS) studies are essential skills for physicians in neonatal intensive care units (NICUs) in order to properly diagnose and manage brain injury. However, these skills are not formally taught to pediatric and neonatal-perinatal medicine (NPM) trainees in Canada. Therefore, our study describes the design, implementation, and evaluation of a new web-based learning (WBL) module that focuses on teaching these skills. METHODS: Trainees' needs assessment survey, sent to all NPM and pediatrics trainees (n = 62), concluded that most of them feel uncomfortable with their ability to interpret CUS, highlighting the need for a new educational intervention. The needs assessment informed the development of the WBL module, which we evaluated using questionnaires and pre-and post-testing methods to measure participants' satisfaction, knowledge gain, skills development, and behaviour changes. Only trainees rotating through the NICU over 6 months (n = 23) were invited to participate in all the evaluation steps. We used the ADDIE instructional design model as a framework for this project. RESULTS: Respondents were very satisfied with the module, and their baseline knowledge increased significantly after studying and engaging with the module. The post-test score was 76% (p < 0.001) compared to the pre-test mean score of 42%. Tests for CUS interpretation skills assessment showed that 49% of pre-test answers were incorrect compared to 8% in the post-test (p < 0.001). Seventy-eight percent of trainees (n = 18) responded to a survey conducted a year after implementation, and 78% of the respondents (n = 14) reported that they still used these skills and shared this knowledge with junior trainees. CONCLUSION: A WBL module for teaching neonatal CUS interpretation considerably improved trainees' knowledge and enhanced their skills in interpreting neonatal CUS.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".