P.069 Pediatric neurology subspecialty education development in a resource limited setting
Bibliographic record
Abstract
Background: In 2013, the University of Calgary (UofC) - Mbarara University of Science and Technology (MUST) Pediatric Education Program was established when the Pediatric Department in Mbarara, Uganda identified a need for enhanced education in pediatric subspecialty areas. We report on the experience of developing the pediatric neurology subspecialty curriculum. Methods: Pre-visit meetings established mutually agreed upon objectives and learning activities that were implemented over 2-week periods in 2015 and 2018. Pre and post-tests were administered to MUST Pediatric residents. Mean differences in test scores were compared using a Student t-test. Residents provided written feedback following the end of the second visit. Results: A pediatric neurologist (AM) visited MUST (2015 and 2018) to deliver the curriculum. The second visit was accompanied by a senior UofC Pediatrics resident (LD). Eight and 14 residents at MUST participated in the curriculum in 2015 and 2018, respectively. Neurology test scores improved in 2015 from a mean of 43% to 61% (p = 0.011) and in 2018 from 53% to 84% (p < 0.00001). Teaching sessions were well received by MUST residents. Conclusions: Collaboration between UofC faculty and MUST established an effective pediatric neurology curriculum that was well-received by residents.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".