INTERNET-BASED INTERVENTIONS FOR CAREGIVERS OF OLDER ADULTS: SYSTEMATIC REVIEW
Bibliographic record
Abstract
Internet-based interventions helping caregivers of older adults to cope with caregiving tasks can offer an efficient and accessible alternative to traditional face-to-face interventions. However, little is known about the existing links between intervention components and outcomes. A systematic review was conducted in databases with keywords relevant to internet, caregivers and self-management interventions. Studies had to report on an intervention delivered mainly using the Internet and on caregiver specific outcomes, include at least one care recipient older than 50 years and score high level of evidence. A narrative synthesis of components (e.g. content, multimedia use, interactive online activities and provision of support), caregiver outcomes (e.g. on stressors, mediators and psychological health) and behavior change techniques was conducted. A total of 2338 articles were screened. 12 randomized controlled trials were included covering 10 internet-based interventions. 5 interventions led to statistically significant results on caregiver outcomes, mostly reporting impacts on depression or anxiety (n=4). From these interventions, 4 incorporated remote professional support with either synchronous (e.g. videoconference, n=1) or asynchronous (e.g. email, n=3) components and all were highly interactive with quizzes on educational content (n=3) or online questionnaires on health status (n=2). All 5 interventions provided instructions for behavior change, 4 provided social support and 3 used modeling techniques. In sum, internet-based interventions that are interactive, model appropriate behavior, incorporate professional support and provide instructions and social support can lead to better outcomes in caregivers. More studies isolating the specific effect of components are needed to better understand the underlying mechanism of action.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".