Factors associated with patient-reported likelihood of using online self-care interventions: a Scleroderma Patient-centered Intervention Network (SPIN) cohort study
Bibliographic record
Abstract
OBJECTIVES: The Scleroderma Patient-centered Intervention Network (SPIN) Cohort uses the cohort multiple randomised controlled trial design to embed trials of online self-care interventions for people living with systemic sclerosis (SSc; scleroderma). To offer interventions to patients interested in using them, participants complete signalling items that query about the likelihood that patients would agree to participate in nine different hypothetical online programmes addressing common SSc-related problems. It is not known what factors influence patient-reported interest in participating in a particular online intervention and if intervention-specific signalling questions provide unique information or replicate broader characteristics, such as overall willingness to participate or self-efficacy. This study assessed factors that explain responses to intervention-specific signalling items. DESIGN: Cross-sectional survey. SETTING: SPIN Cohort participants enrolled at 42 centres from Canada, the USA, the UK, France, Spain and Mexico who completed study questionnaires from March 2014 to January 2018 were included. MEASURES: Demographic and disease characteristics, self-efficacy and symptoms related to each specific intervention were completed in addition to signalling items. General likelihood of using interventions was calculating by taking the mean score of the remaining signalling questions. PARTICIPANTS: 1060 participants with complete baseline data were included in the analyses. RESULTS: For all individual signalling questions, controlling for other variables, the mean of the remaining signalling questions was the strongest predictor (standardised regression coefficient β from 0.61 (sleep) to 0.80 (self-management)). Smaller, but statistically significant, associations were found with the symptom associated with the respective signalling question and with general self-efficacy for 7 of 9 signalling questions. CONCLUSIONS: The main factor associated with patients' interest in participating in a disease-specific online self-care intervention is their general interest in participating in online interventions. Factors that may influence this general interest should be explored and taken into consideration when inviting patients to try online interventions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".