IMPACT OF INTERNET-BASED INTERVENTIONS ON MENTAL HEALTH OF CAREGIVERS OF ADULTS WITH CHRONIC CONDITIONS
Bibliographic record
Abstract
Family caregivers provide important sources of support to older community-living adults with chronic conditions. However, caregivers often experience negative mental health outcomes as a result of caregiving. Internet-based interventions have the potential to mitigate the negative mental health outcomes associated with caregiving. The objective of this systematic review and meta-analysis was to examine the impact of internet-based interventions on caregiver mental health outcomes, and the impact of different types of internet-based intervention programs. Multiple databases were searched for relevant randomized controlled trials or controlled clinical trials that compared internet-based intervention programs with no or minimal internet-based interventions. Title and abstract, and full-text screening were completed in duplicate. Data were extracted by a single reviewer and verified by a second reviewer, and risk of bias assessments were completed accordingly. Where possible, data for mental health outcomes were meta-analyzed using standardized mean differences. Of 7,923 unique citations, 13 studies met the inclusion criteria. Beneficial effects of any internet-based intervention program resulted in a mean decrease of 0.48 points (95% CI: -0.75 to -0.22) for stress/distress among caregivers and a mean decrease of 0.40 points (95% CI: -0.58 to -0.22) for anxiety among caregivers. For studies that examined internet-based information/education and internet-based information/education plus professional psychosocial support, the meta-analysis results showed small to medium effect sizes for the mental health outcomes of depression, stress/distress and anxiety. Given the limited quality of the included studies, further high-quality research is needed to inform the effectiveness of interactive, dynamic, and multi-component internet-based interventions for caregivers.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".