Mixed Methods Survey to Identify Barriers to Accessing Deep Brain Stimulation for Movement Disorders in Canada
Bibliographic record
Abstract
BACKGROUND: Movement disorders (Parkinson's disease, essential tremor, dystonia) are debilitating, progressive conditions that profoundly impact patients' quality of life. Surgical therapies, such as deep brain stimulation (DBS) can provide tremendous relief to patients but remain costly and, therefore, limited in availability. It is critical to understand regional barriers to accessing this service to improve access for all patients who may benefit from it. METHODS: This is a mixed methods survey of stakeholders (patients/family members, advocacy groups, family physicians, neurologists, neurosurgeons) assessing perceived barriers to DBS for movement disorders. Closed and open-ended questions were used. Descriptive statistics were used to highlight regions of Canada where perceived access is poor and to identify barriers to access. RESULTS: A total of 220 responses were recorded (12 neurosurgeons, 22 neurologists, 30 family physicians, 153 patients and caregivers and 3 advocacy group personnel). Themes included limited resources/centralization of resources, education, burdensome referral patterns and personal patient factors. Barriers included costs associated with travel, waitlists, lack of specific resources and poor understanding of movement disorders, DBS indications and referral pathways. CONCLUSIONS: A number of barriers to access to DBS have been identified, related to geography and centralization of services, referrals and need for further education of indications and safety. The use of virtual care, centralized referral pathways and further research to determine the true prevalence of candidates for this therapy are required to improve access to DBS for movement disorders in Canada.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".