Effectiveness of an online fatigue self-management programme for people with chronic neurological conditions: a randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate an online fatigue self-management programme in a sample of adults with chronic neurological conditions. DESIGN: Randomized controlled trial. SETTING: Online fatigue self-management programme delivered across Australia. PARTICIPANTS: Ninety-five people with fatigue secondary to multiple sclerosis, Parkinson's disease or post-polio syndrome. INTERVENTIONS: An online fatigue self-management programme, an information-only fatigue self-management programme and a control group. MAIN MEASURES: Groups were compared at pre test, post test and at three months on primary outcomes using the Fatigue Impact Scale, Activity Card Sort and Personal Wellbeing Index. RESULTS: With the exception of the Personal Wellbeing Index at post test (F = 3.519; P =0.034) and the Physical Subscale of the Fatigue Impact Scale at follow-up (F = 3.473; P =0.035) there were no significant differences between the three groups on primary outcomes. Post-hoc testing showed the differences to be between the information-only and control groups (P = 0.036 and P = 0.030 respectively). Improvement in the information-only group was unexpected but appears to be similar to results of other online interventions. The fatigue self-management and information-only groups performed better than the control on some secondary outcome measures. Low power in the analysis may have contributed to the findings. Repeated-measures ANCOVA showed that the fatigue self-management and the information-only groups improved over time on the Fatigue Impact Scale and the Activity Card Sort (P<0.05). The control group showed no improvements over time. CONCLUSIONS: Although the fatigue self-management group improved over time, results did not demonstrate additional benefit in most outcome measures when compared with the control group.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".