Preparing the next generation of paediatricians: The importance of clinical informatics education
Bibliographic record
Abstract
As healthcare becomes more reliant on technology, it is crucial that we train the next generation of paediatricians to be proficient in clinical informatics. At the Hospital for Sick Children and University of Toronto, we have developed a clinical informatics elective that aims to provide paediatric residents with the skills and knowledge they need to effectively use technology in the delivery of care. The core of the elective is a 2-week or 4-week program that includes meetings with informatics leaders at the hospital, core readings, and core deliverables. These deliverables are designed to help residents reflect on their learning about clinical informatics and its role in healthcare, and how their experiences will influence their future careers. One of the key goals of the elective is to introduce residents to the field of clinical informatics, highlighting areas of growth, quality improvement and more. The elective also covers the Epic Electronic Health Record system, including its strengths and weaknesses, and explores the role of information services (IS) and governance in clinical informatics. Additionally, residents have the opportunity to learn about the IS structure at the hospital and how clinical informatics relates to IS.
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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 | 0.018 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.034 | 0.010 |
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".