Implementation of Cognition, Fatigue, and Mental Health Screening at a Canadian Multiple Sclerosis Center: A Quality Improvement Initiative
Bibliographic record
Abstract
Background: Mental health concerns, cognitive impairment, and fatigue are prevalent among Canadians with multiple sclerosis (MS), yet many neurology clinics are not equipped to adequately address these concerns. In this quality improvement initiative, low-burden systems were implemented at an MS clinic with the aim of improving the identification of these invisible symptoms among people with MS. Methods: Self-report questionnaires assessing anxiety, depression, and fatigue were electronically administered to patients prior to clinic appointments. When moderate mental health symptoms were reported, clinicians were alerted by a best practice advisory and directed to provide the patient with a list of local low- and no-cost mental health services. An MS-sensitive cognitive task was also administered via a tablet upon check-in to patients meeting with the physician assistant (PA). Data were collected from appointment notes over 10 clinic days before and after implementation. Results: From pre- to post implementation, the proportion of patients who received formal mental health screening increased from 0% to 54.55%, and the proportion of patients with mental health concerns who were offered resources increased from 23.53% to 47.83%. The proportion of patients who received formal fatigue screening increased from 0% to 22.46%. The rate of cognitive testing in visits with the PA increased from 33.33% to 85.11%. Conclusions: This initiative improved mental health, cognitive, and fatigue screening and established a foundation for ongoing quality improvement initiatives to better meet best practice recommendations for comprehensive MS care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".