Good multiple sclerosis (MS) care and how to get there in Canada: Perspectives of Canadian healthcare providers working with persons with MS
Bibliographic record
Abstract
Objective: The literature that has examined healthcare access and needs of the multiple sclerosis (MS) population is limited. Currently, no research has engaged healthcare providers delivering services to this population to examine their perspectives on the provision of MS care in Canada. We aimed to summarize what good MS care should look like according to Canadian healthcare providers working with people with MS, and to identify the supports and resources required, within their care setting, to enable this standard of care. Methods: A qualitative descriptive approach was taken to analyze data from participants who responded to additional open-ended survey questions, within a larger "MS Models of Care Survey" targeting Canadian healthcare providers working with persons with MS. Results: Currently, a gap exists between what healthcare providers working with persons with MS believe MS care should encompass and what they are able to offer. Participants emphasized that their MS clinics are currently understaffed and patient-to-provider ratios are high, leaving very little time to address the array of healthcare concerns their patients present with. The healthcare providers overwhelmingly described that moving toward multidisciplinary team-based MS care that includes appropriate numbers of MS-trained neurologists, nurses, physiotherapists, occupational therapists, and mental health providers working within one location would be their prioritized approach to comprehensively managing MS care. This model of care enables all professionals to effectively coordinate care and use their time efficiently by only focusing on their area of expertise, all while meeting the needs of their patient in one setting, reducing wait-times and improving overall care. Conclusion: To meet the care needs of Canadians with MS, the healthcare system must consider standardizing and funding multidisciplinary team-based MS clinics, comparable to Stroke units, which continue to show favorable health outcomes after years of implementation.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.040 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".