Enrolling and keeping participants in multiple sclerosis self-management interventions: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: The objectives were to provide an estimate of expected enrolment and attrition rates based on published studies of existing self-management interventions for people with multiple sclerosis, and to identify contributing factors and impact on outcomes. REVIEW METHODS: A systematic literature search was conducted using Ovid MEDLINE, PsychINFO, EMBASE, AMED, CINAHL, OT Seeker, PubMed, and the Cochrane Database of Systematic Reviews databases. Controlled trials with or without randomization using either a between-group or within-person design were included if they met specified criteria. A random-effect meta-regression analysis was conducted to estimate the overall enrolment and attrition proportions, effect of person- and study-related factors, and impact on outcomes. RESULTS: A total of 48 studies, comprising 4446 persons were identified. The estimated enrolment rate was 50.3% (95% confidence interval (CI): 49.6 to 51.1) and the estimated attrition rates in the intervention and control groups were 16.8% (95% CI: 16.2 to 17.3) and 14.4% (95% CI: 13.8 to 14.9), respectively. The main reported reason for refusing to participate was lack of interest (70.6%), while the reported reasons for dropping out were mainly owing to medical issues (26.1%) and disliking the intervention (17.9%). Trial, programme, and patient-related variables were found to influence the enrolment and/or attrition rates. Studies that had a 10% higher attrition rate had an effect size that was larger by 0.19 (95% CI: 0.17 to 0.24). CONCLUSION: Greater understanding of the factors associated with enrolment and attrition rates would help in planning and developing a more appealing self-management intervention that patients can easily accept and incorporate into their everyday lives.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".