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 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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".