Factors influencing how informal caregivers of people with multiple sclerosis access and use a curated intervention website: Analysis from an RCT
Bibliographic record
Abstract
Objective Healthcare consumers and providers are increasingly turning to digital solutions, such as curated websites. Knowing who accesses/benefits from these may improve design and development. This study investigated website usage of informal caregivers of people with multiple sclerosis and shifts in outcome plausibly associated with usage. Methods Secondary analysis of data from a randomized clinical trial of 148 caregivers compared effectiveness of a website + tele-coaching to a website only intervention for caregivers. Groupwise differences in means/proportions were tested using t-tests and chi-square. Modified Poisson regression with a robust variance estimator and ordinal logistic regression tested the relationship between group and likelihood of website log-in. Ordinal logistic regression models examined whether caregiver characteristics were associated with website use. Generalized estimating equations (GEE) with an autoregressive correlation structure modeled the relationship between website usage and outcomes. Results Females were more likely to access the website than males (60% vs. 43%; p = 0.05). Though not statistically significant, a possible association (POR: .85, 95% CI: .69, 1.03) between caregiver burden and website access emerged; caregivers experiencing highest levels of burden appeared less likely to engage. Usage patterns differed by treatment arm: the website-only group accessed the Caring for yourself topic significantly more (61.67% vs. 38.33%: p = .04) with similar, but insignificant, trends for other topics. Conclusions Clinicians can be confident referring females with moderate levels of burden to website-based interventions. By contrast, male caregivers and those experiencing high levels of burden may be less likely to access these resources, pointing to the need for alternative interventions. Trial Registration Clinicaltrials.gov, registration number: NCT0466208.
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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 | 0.052 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".