Epilepsy is associated with unmet health care needs compared to the general population despite higher health resource utilization—A Canadian population‐based study
Bibliographic record
Abstract
PURPOSE: (1) To determine whether health resource utilization (HRU) and unmet health care needs differ for individuals with epilepsy compared to the general population or to those with another chronic condition (asthma, diabetes, migraine); and (2) to assess the association among epilepsy status, sociodemographic variables and HRU. METHODS: Data on HRU were assessed using the 2001-2005 Canadian Community Health Surveys, a nationally representative population-based survey. Weighted estimates of association were produced as adjusted odds ratio with 95% confidence intervals, and logistic regression was used to explore the association between sociodemographic variables and HRU in those with epilepsy. All data on disease status, HRU, and unmet health care needs were self-reported. KEY FINDINGS: Individuals with epilepsy had the highest rate of hospitalizations and the highest mean number of consultations with physicians. Despite higher rates of consultation with psychologists and social workers compared to the general population, those with epilepsy were significantly more likely to say they had unmet mental health care needs. People with epilepsy were also less likely to use dental services compared to the general population. Epilepsy was a significant predictor of HRU in logistic regression models. SIGNIFICANCE: Given the prevalence of psychiatric comorbidities in those with epilepsy, it is concerning that this group perceives unmet mental health care needs. It is also troublesome that there was decreased utilization of dental health care resources in those with epilepsy considering that these patients are more likely to have poor oral health. Although individuals with epilepsy use more health care services than the general population, this increase appears to be insufficient to address their health care needs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".