The Effectiveness of Pediatric Residency Education in Preparing Graduates to Manage Neurological and Neurobehavioral Issues in Practice
Bibliographic record
Abstract
PURPOSE: Pediatricians are required to manage a variety of neurodevelopmental conditions. In this study, the authors surveyed self-assessment of competency by recent graduates of the University of British Columbia (UBC) pediatric residency training program in various areas of pediatric neurology, from acute care to behavioral assessments. METHOD: Forty-six questionnaires were mailed or e-mailed between October 2002 and September 2004 to UBC pediatrics program graduates of 1998-2004. The 29-item questionnaire consisted of 26 questions on a six-point scale and asked respondents to rate their ability to manage specific neurological and developmental symptoms and conditions. Also, three open-ended questions asked respondents which topics required less emphasis or more emphasis and which were appropriate for continuing medical education (CME). Descriptive statistical analyses were performed. RESULTS: A total of 39 questionnaires (85% response rate) were completed and returned. The results indicate that 74% of pediatric graduates feel that more of the neurology portion of residency training should be spent on general pediatric behavioral and neurological conditions (e.g., attention-deficit/hyperactivity disorder or developmental delay) and would choose to attend CME on these areas, and 28% of respondents would prefer to receive less training on complex neurological problems that are managed at a subspecialty level (e.g., chronic refractory seizures). CONCLUSIONS: Pediatric residents need sufficient, specific training to enable them to competently investigate and manage the neurobehavioral complaints and conditions commonly encountered by general pediatricians in both their office and hospital practice.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".