Identifying the educational needs of physicians in pediatric epilepsy in order to improve care: results from a needs assessment in Germany, Spain, and the United States
Bibliographic record
Abstract
The objective of this study was to gather evidence-based data on the educational needs of neuropediatricians. A needs assessment was conducted to identify the clinical challenges of physicians when diagnosing, medically treating, and managing pediatric patients with epilepsy; which could be addressed through educational interventions. A two-phase mixed-methods approach was used to conduct the needs assessment in Germany, Spain, and the US. Phase 1 consisted of qualitative data collection through multiple sources: a literature review, semi-structured interviews with clinicians and nurses working in pediatric epilepsy, and interpretation and input from faculty experts. Qualitative data were coded (NVivo) and analyzed using a thematic analysis, and findings were then used to design the second phase. Phase 2 consisted of quantitative data collection through an online survey that aimed to validate the identified challenges and underlying causes using a larger sample than in Phase 1. Data from the survey were analyzed using frequency tabulations and chi-square tests (SPSS). A total of 267 participants were included in the study. Phase 1 included 88 participants (neurologists, pediatricians, neuropediatricians, and nurses). Phase 2 included 179 participants (neurologists, pediatricians, and neuropediatricians). The main areas of challenge which emerged from the triangulated data included: the integration of guidelines into practice, identification of epilepsy and epilepsy events, integration of genetic testing into practice, integration of non-pharmacological treatments, transition from pediatric to adult care, and involvement and engagement with caregivers. Underlying causes of these challenges are reported, along with supporting qualitative findings. This study identified the educational needs of physicians working in pediatric epilepsy in Germany, Spain and the USA. Increasingly, educational interventions are required to be evidence-based. The results of this study could be used to design such interventions to support neuropediatricians who wish to specialize in pediatric epileptology, in order to manage the identified challenges.
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".