The role of internet-based digital tools in reducing social isolation and addressing support needs among informal caregivers: a scoping review
Bibliographic record
Abstract
BACKGROUND: In Canada, 8.1 million people informally provide care without payment, primarily to family members; 6.1 million of them are employed at a full-time or part-time job. Digital technologies, such as internet-based tools, can provide informal caregivers' access to information and support. This scoping review aimed to explore the role of internet-based digital tools in reducing social isolation and addressing support needs among informal caregivers. METHODS: A systematic search for relevant peer-reviewed literature was conducted of four electronic databases, guided by Arksey and O'Malley's framework. An extensive search for relevant grey literature was also conducted. RESULTS: The screening process yielded twenty-three papers. The following themes were generated from the reviewed studies: searching for and receiving support; gaining a sense of social inclusion and belonging; and benefits and challenges of web-based support. The studies noted that, to connect with peers and obtain social support, informal caregivers often turn to online platforms. By engaging with peers in online communities, these caregivers reported regaining a sense of social inclusion and belonging. CONCLUSIONS: The findings suggest that internet-based digital tools can be a cost-effective and convenient way to develop programs that help unpaid caregivers form communities, gain support, and access resources. Service providers can leverage digital tools to deliver support to caregivers within online communities.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".