Mapping the Landscape of Equitable Access to Advanced Neurotechnologies in Canada
Bibliographic record
Abstract
ABSTRACT: Geographic, social, political, and economic factors shape access to advanced neurotechnologies, yet little previous research has explored the barriers, enablers, and areas of opportunity for equitable and meaningful access for diverse patient communities across Canada. We applied a mixed-mode approach involving semi-structured interviews and rating scale questions to consult with 24 medical experts who are involved in the care of patients who undergo functional neurosurgery targeting the brain. Seven major themes emerged from the qualitative analysis: Health care system, Neurotechnology features, Patient demographics, Target condition features, Ethics, Upstream barriers and enablers, and Areas of opportunity. Descriptive statistics of the Likert-scale responses suggest that interviewees perceive a disparity between the imperative of access to advanced neurotechnologies for people living in rural and remote areas and the likelihood of achieving such access. The results depict a complex picture of access to functional neurosurgery in Canada with pockets of excellence and a motivation to improve the availability of care for vulnerable populations through the expansion of distributed care models, improved health care system efficiencies, increasing funding and support for patient travel, and increasing awareness about and advocacy for advanced neurotechnologies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| 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".