The international league against epilepsy primary healthcare educational curriculum: Assessment of educational needs
Bibliographic record
Abstract
OBJECTIVE: To assess the need for an epilepsy educational curriculum for primary healthcare providers formulated by the International League Against Epilepsy (ILAE) and the importance attributed to its competencies by epilepsy specialists and primary care providers and across country-income settings. METHODS: The ILAE primary care epilepsy curriculum was translated to five languages. A structured questionnaire assessing the importance of its 26 curricular competencies was posted online and publicized widely to an international community. Respondents included epilepsy specialists, primary care providers, and others from three World Bank country-income categories. Responses from different groups were compared with univariate and ordinal logistic regression analyses. RESULTS: Of 785 respondents, 60% noted that a primary care epilepsy curriculum did not exist or they were unaware of one in their country. Median ranks of importance for all competencies were high (very important to extremely important) in the entire sample and across different groups. Fewer primary care providers than specialists rated the following competencies as extremely important: definition of epilepsy (p = .03), recognition of seizure mimics (p = .02), interpretation of test results for epilepsy care (p = .001), identification of drug-resistant epilepsy (0.005) and management of psychiatric comorbidities (0.05). Likewise, fewer respondents from LMICs in comparison to UMICs rated 15 competencies as extremely important. SIGNIFICANCE: The survey underscores the unmet need for an epilepsy curriculum in primary care and the relevance of its competencies across different vocational and socioeconomic settings. Differences across vocational and country income groups indicate that educational packages should be developed and adapted to needs in different settings.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".