30th International Epilepsy Congress, Montreal, Canada, 23–27 June, 2013
Bibliographic record
Abstract
Purpose: To develop and implement an epilepsy education and advocacy program for frontline healthcare providers and key community members in rural Tanzania.The districts of Kilombero and Ulanga have a high prevalence of epilepsy and treatment gap of 95-100% largely due to misconceptions of seizures and knowledge gap at the community level.Method: In order to raise awareness and decrease stigma about epilepsy, identify community members with seizures and increase the number of persons with epilepsy receiving treatment; an epilepsy education and awareness program will be implemented in rural Tanzania.Pre and post design will be used to evaluate the benefits of the intervention.Participants will then further disseminate this information to their communities including local leaders, healers and community members.Community assessments of perceptions of epilepsy pre and post intervention will be utilized to assess for a change in perception of those living with epilepsy.Results: To date a needs assessment has been completed in the districts of Ulanga and Kilombero in rural Tanzania reinforcing the position of the International League Against Epilepsy of the need for increased awareness, understanding and treatment of epilepsy in Africa.BE-Beyond Epilepsy is an educational and advocacy program developed in collaboration with the Tanzania National Nurses Association (TANNA), Tanzania Training Center for International Health (TTCIH) and the Tanzania Ministry of Health. Conclusion:The community needs assessment has demonstrated a desire for this initiative amongst frontline healthcare providers, healers, persons with epilepsy and community leaders.The pilot project will be implemented in January 2014 with 25 frontline healthcare providers and key community members.Program implementation will take place at the TTCIH with the support of TANNA and the Ministry of Health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.264 | 0.103 |
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".