Bibliographic record
Abstract
Researchers see a definite link between health and economic statuses, but relatively little is known about the economic status among people with particular chronic diseases. Less is known about the economic status and living conditions of people with epilepsy. The aim of this study was to evaluate the economic status and living conditions among people who suffer from epilepsy in Lithuania. The article is based on the data of empirical research that was carried out in 2014. During this research, 220 people with epilepsy, aged 18–85 years, were investigated. The evaluation of some aspects of economic status and living conditions of people with epilepsy, such as personal and household income (income responses were based on subjective measures), sources of household income, housing quality, durable goods and some aspects of financial capability are presented in this paper. The main findings of research were that income differentiation among people with epilepsy is high. Income disparities between rural and urban areas, like between different educational levels, are observed. The vast majority of income of people with epilepsy is low and it represents a significant element of their household income. A quarter of people who have epilepsy cannot afford to buy new clothes and every second person cannot replace his/her worn-out furniture. Even a tenth of people with epilepsy cannot afford to eat meat, chicken and fish (or a vegetarian equivalent dish) every second day. An absence of durable goods is more associated with the personal choice of people with epilepsy rather than with a lack of resources. The obtained results show that people with epilepsy dutifully pay all their monthly bills (utility bills, rent and/or loans to credit payments), but more than a quarter of them lack the necessary resources to accumulate money for the future (their money always runs out and they never have any additional left over) and the largest group of people with epilepsy (30%) thought that they would manage for less than one month if they faced a drop in income.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".