Evaluating occupational performance coaching to support fatigue management for people with multiple sclerosis: A feasibility study
Bibliographic record
Abstract
Objective To determine the feasibility of adding coaching sessions to a website (MS INFoRM) that supports self-directed fatigue management for people with multiple sclerosis (PwMS). Design Double-blind, parallel-group feasibility study. Participants and setting Twenty-six PwMS, who experienced severe fatigue (fatigue severity scale > 5.4), were recruited from participants who were ineligible for the main trial testing on the MS INFoRM website. Intervention Six 45-to-60-min sessions of one-on-one coaching plus access to the MS INFoRm website compared to two check-in phone calls plus access to the MS INFoRm website. Both study arms took place over 3 months. Main measures Feasibility parameters included proportion eligible of those screened; proportion consented; missing data; retention and adherence rates. Acceptability was explored through qualitative interviews. Secondary outcomes (self-efficacy and fatigue impact) were measured at baseline and post-intervention. Results 76 people were invited to participate in this add-on study. 40 were interested and screened: 32 were eligible, 26 consented, and were randomized (mean age: 48.5 yrs (SD: 8.7), mean disease duration: 11.5 yrs). Retention was 85% (22 out of 26). Coaching adherence was high (86% attended ⩾ 5 sessions). At 3 months, people in the intervention group showed more improvements in self-efficacy and fatigue impact compared to the comparison group, however, the difference was not statistically significant ( p = 0.471 and p = 0.147, respectively). The intervention was well-received by the participants and there were no adverse events. Conclusion Combining one-on-one coaching sessions along with web-based interventions is feasible and appreciated by the participants, and worth exploring further in a larger trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".