Measuring the effects of nurse practitioner (NP)-led care on depression and anxiety levels in people with multiple sclerosis: a study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Canada has one of the highest rates of multiple sclerosis (MS) in the world. Treatments and supports for people with MS (PwMS) have become increasingly complex, requiring individualized and adaptive care. Specialized NPs provide advanced skills to those with complex medical conditions, with potential to enhance the health, functioning, and quality of life for PwMS. This study aims to determine the effect of a Nurse Practitioner (NP) on depression and anxiety levels in PwMS. METHODS: We will perform a parallel randomized controlled trial. PwMS who are followed by general private-practice neurologists will be randomly assigned to the intervention group (NP-led care) or the 'usual care' control group (general neurologist or family physician and registered nurse support). In the intervention group, the NP will assess and provide care to the MS patient and their caregiver at a baseline visit, with 3-month and 6-month follow-up visits. PwMS in the control group will receive usual care provided by their community neurologists or family physicians with the standard assistance provided by registered nurses experienced in MS care. The primary outcome will be the difference in change in the patient's anxiety and depression scores as measured by the validated Hospital Anxiety and Depression Scale (HADS) questionnaire at 3 months. Secondary outcomes will include difference in change in HADS at 6 months; Modified Fatigue Impact Scale scores (MSIF) at 3 and 6 months; EQ-5D scores at 3 and 6 months; caregiver health-related quality of life in MS measures (CAREQOL-MS) at 3 and 6 months; number of visits and phone calls to healthcare professionals recorded by patient, and satisfaction with NP-led care vs usual care measured by the validated Consultant Satisfaction Questionnaire. DISCUSSION: Findings from this study will contribute to exploring benefits of advanced nursing practitioner interventions for PwMS followed by general neurologists and family physicians in a community setting. It will provide evidence of the benefits of NP-led care for PwMS and offer an alternative healthcare resource for management of MS. TRIAL REGISTRATION: ClinicalTrials.gov Pro00069595 . Retrospectively registered on June 26, 2020. Protocol version: January 2017, version 1.
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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 | 0.045 | 0.037 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".