Report from the child neurology education and training workshop at the International Child Neurology Congress 2024: Expert's addressing the training gap
Bibliographic record
Abstract
This report summarizes the key findings of a workshop undertaken at the International Child Neurology Congress in 2024 by child neurologists with expertise in training education and invested colleagues. The workshop aimed to explore global issues which have impact on access to child neurology training. The major findings supported a great need for more training programs globally, that consensus is needed for the minimum standards of training, and that training programs can be strengthened via global health partnerships especially with collaborations from regions with more available resources. The group concurred that the phenomena of 'neurophobia' amongst general paediatricians and medical trainees, was a reality, and creates barriers both working with paediatric colleagues, as well as recruiting specialists to the field. Optimal teaching practices for child neurology should include the expansion of learning through global partnerships and virtual educational resources. Measures must be put into place for fledgling training programs, to support colleagues in less resourced settings and to avoid their burn-out. Collegial and collaborative work is essential to support the future of child neurology across the globe, both to reach the current capacity needs but also to meet the necessary growth in the field.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.039 | 0.023 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".