Online Educational Tools for Caregivers of People with Dementia: A Scoping Literature Review
Bibliographic record
Abstract
BACKGROUND: Informal caregivers of people with dementia provide the majority of health-based care to people with dementia. Providing this care requires knowledge and access to resources, which caregivers often do not receive. We set out to evaluate the effect of online educational tools on informal caregiver self-efficacy, quality of life, burden/stress, depression, and anxiety, and to identify effective processes for online educational tool development. METHODS: We conducted a scoping review of articles on online educational interventions for informal caregivers of people with dementia searching CINAHL, MEDLINE, EMBASE, and PubMed from 1990 to March 2018, with an updated search conducted in 2020. The identified articles were screened and the data were charted. RESULTS: 33 articles that reported on 24 interventions were included. There is some evidence that online interventions improve caregiver-related outcomes such as self-efficacy, depression, dementia knowledge, and quality of life; and decrease caregiver burden. Common findings across the studies included the need for tailored, stage-specific information applicable to the caregiver's situation and the use of psychosocial techniques to develop the knowledge components of the interventions. CONCLUSION: We demonstrate the importance of having caregivers and health-care professionals involved at all stages of tool conceptualization and development. Online tools should be evaluated with robust trials that focus on how increased knowledge and development approaches affect caregiver-related outcomes.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".