Is insufficient knowledge of epilepsy the reason for low levels of healthcare in the Lao PDR?
Bibliographic record
Abstract
BACKGROUND: The treatment gap for epilepsy is considerable in low and middle-income countries. In the Lao PDR it is estimated at over 90%. Health workers play a significant role in bridging the gap between people with epilepsy (PWE) and access to epilepsy care. In a national survey we assessed: 1) the knowledge and practices of health workers in the Lao PDR towards the disease, and, 2) patient attendance at health facilities. METHODS: We conducted a random three-stage sampling of health workers at the provincial, district and health center levels in 2009. RESULTS: Overall, 284 health workers were enrolled in 50 health facilities of 11 provinces: health centers 24.7%; district hospitals 23.2%; and province hospitals 52.1%. Only a minority of these (2.5%) recalled ever having received training or seeking information on epilepsy. Our survey showed a lack of knowledge in diagnosing and prescribing drugs for epilepsy, including phenobarbital, the first-line of treatment. The majority of respondents (59.9%) was unaware of the availability of antiepileptic drugs in health facilities. Only 10 (20%) health facilities, and no health centres, received people with epilepsy. It was estimated that one PWE per month receives medical attention. Traditional beliefs about PWE were common; such as the idea that epilepsy could be transmitted through saliva (63.2%). A higher attendance of PWE was observed in province hospitals where the knowledge of epilepsy care was higher. Global acceptance of people with epilepsy was low. CONCLUSIONS: The low level of knowledge of epilepsy on the part of health workers may be contributing to the wide treatment gap in the Lao PDR. Improving knowledge of this disease and increasing the availability of antiepileptic drugs will reduce misconceptions about epilepsy, thus encouraging more PWE to seek treatment. Community-based educational programs and extensive advocacy for people with epilepsy only began in 2009.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".