A pilot mixed-methods evaluation of MS INFoRm: a self-directed fatigue management resource for individuals with multiple sclerosis
Bibliographic record
Abstract
Fatigue management interventions for individuals with multiple sclerosis (MS) often feature structured programmes requiring repeated, in-person attendance that is not possible for all individuals. We sought to determine whether MS INFoRm, a self-directed fatigue management resource for individuals with MS, was worth further, more rigorous evaluation. Our indicators of worthiness were actual use of the resource by participants over 3 months, reductions in fatigue impact and increases in self-efficacy, and participant reports of changes in fatigue management knowledge and behaviours. This was a single-group, mixed-methods, before-after pilot study in individuals with MS reporting mild to moderate fatigue. Thirty-five participants were provided with MS INFoRm by a USB flash drive to use at home for 3 months, on their own volition. Twenty-three participants completed all standardized questionnaires, semi-structured interviews and study process measures. Participants reported actively using MS INFoRm over the 3-month study period (median total time spent using MS INFoRm=315 min) as well as significantly lower overall fatigue impact (Modified Fatigue Impact Scale: t=2.6, P=0.01), increased knowledge of MS fatigue (z=-2.8, P=0.01) and greater confidence in managing MS fatigue (z=-3.3, P=0.001). Individuals with significant reductions in fatigue impact also reported behavioural changes including tracking fatigue, better communication with others, greater awareness, improved quality of life and being more proactive. This study provides evidence that further rigorous evaluation of MS INFoRm, a self-directed resource for managing fatigue, is worth pursuing.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".