Characterizing the diversity of the multiple sclerosis population in Canada: A scoping review
Bibliographic record
Abstract
Background: This scoping review aimed to identify existing information and gaps in knowledge regarding the diversity characteristics of the multiple sclerosis (MS) population in Canada. Methods: We searched MEDLINE, EMBASE, Cumulated Index in Nursing and Allied Health Literature (CINAHL), SCOPUS and ProQuest's global dataset of theses and dissertations from 2010 to January 12, 2024. Data sources were case reports/series, cohort studies, case-control studies, analytical cross-sectional studies, randomized clinical trials, qualitative, mixed methods, participatory studies and systematic reviews conducted in Canada, published in English or French, that included participants with clinically isolated syndrome or MS. Sample characteristics were extracted applying Cochrane's PROGRESS-Plus framework. Results: We included 259 studies, most often studying disease-modifying therapy (24.3%) and access to care (20.9%). Among primary data collection studies 40% used one recruitment strategy, usually MS Clinics and MS Canada. Age (92.7%) and sex (86.9%) were reported most often, ≤10% of studies reported race or ethnicity; religion, sexual orientation and language were not reported. Conclusions: We lack an understanding of characteristics of people living with MS in Canada relevant to health equity. Existing research has been insufficiently inclusive. Better reporting of diversity characteristics is needed, along with specific efforts to recruit and retain more diverse samples.
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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 | 0.028 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.045 | 0.076 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".