Self-Management Support and eHealth When Managing Changes in Behavior and Mood of a Relative With Dementia: An Asynchronous Online Focus Group Study of Family Caregivers' Needs
Bibliographic record
Abstract
The current article discusses how and by whom family caregivers want to be supported in self-management when managing changes in behavior and mood of relatives with dementia and whether family caregivers consider eHealth a useful tool for self-management support. Four asynchronous online focus groups were held with 32 family caregivers of individuals with dementia. Transcripts of the online focus groups were analyzed using qualitative thematic analysis. Family caregivers need support from professionals or peers in the form of (a) information about dementia and its symptoms, (b) tips and advice on managing changes in behavior and mood, (c) opportunities to discuss experiences and feelings, and (d) appreciation and acknowledgement of caregiving. The opinions of family caregivers about self-management support through eHealth were also reported. Findings suggest a personal approach is essential to self-management support for family caregivers managing changes in behavior and mood of relatives with dementia. In addition, self-management support can be provided to some extent through eHealth, but this medium cannot replace personal contacts entirely. [Res Gerontol Nurs. 2018; 11(3):151-159.].
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".