An Overview of Multiple Sclerosis Care in Rural and Urban Newfoundland and Labrador
Bibliographic record
Abstract
INTRODUCTION: Limited access to multiple sclerosis (MS)-focused care in rural areas can decrease the quality of life in individuals living with MS while influencing both physical and mental health. METHODS: The objectives of this research were to compare demographic and clinical outcomes in participants with MS who reside within urban, semi-urban and rural settings within Newfoundland and Labrador. All participants were assessed by an MS neurologist, and data collection included participants' clinical history, date of diagnosis, disease-modifying therapy (DMT) use, measures of disability, fatigue, pain, heat sensitivity, depression, anxiety and disease activity. RESULTS: Overall, no demographic differences were observed between rural and urban areas. Furthermore, the categorization of primary residence did not demonstrate any differences in physical disability or indicators of disease activity. A significantly higher percentage of participants were prescribed platform or high-efficacy DMTs in semi-urban areas; a higher percentage of participants in urban and rural areas were prescribed moderate-efficacy DMTs. Compared to depression, anxiety was more prevalent within the entire cohort. Comparable levels of anxiety were measured across all areas, yet individuals in rural settings experienced greater levels of depression. Individuals living with MS in either an urban or rural setting demonstrated clinical similarities, which were relatively equally managed by DMTs. CONCLUSION: Despite greater levels of depression in rural areas, the results of this study highlight that an overall comparable level and continuity of care is provided to individuals living with MS within rural and urban Newfoundland and Labrador.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".