Health and Community-Based Services for Individuals with Neurological Conditions
Bibliographic record
Abstract
BACKGROUND: The current study involves a national survey of healthcare providers who offer services for individuals with a variety of neurological conditions. It aims to describe the provision of health and community-based services as well as the admission criteria, waitlist practices, and referral sources of these services. METHODS: An online survey was directed at administrators/managers from publicly funded hospital programs, long-term care homes, and community-based healthcare provider agencies that were believed to be providing information and/or services to patients with a variety of neurological conditions. RESULTS: Approximately 60% (n=254) of respondents reported providing services in either urban/suburban areas or rural/remote areas only, whereas the remaining 40% (n=172) provided services regardless of patient location. A small proportion of respondents reported providing services for individuals with dystonia (28%), Tourette syndrome (17%), and Rett syndrome (13%). There was also a paucity of diverse healthcare professionals across all institutions, but particularly mental healthcare professionals in hospitals. Lastly, the majority of respondents reported numerous exclusion criteria with regard to service provision, including prevalent comorbid conditions. CONCLUSIONS: If the few services provided for these neurological patient populations exclude common comorbidities, it is likely that there will be no other place for these individuals to seek care.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".