"My tools for care": a self-administered online intervention to support family carers of older persons with dementia
Bibliographic record
Abstract
Background Considering the important role of family carers of persons living with some form of dementia, it is critically important to support carers. Current examples of interventions that have been evaluated to support family carers (largely based on stress and burden theories) include training and education sessions or coping and stress reduction programs led by healthcare professionals. These interventions have shown limited reduction of burden for family carers. Given the journey family carers undertake, transition theory provides an appropriate and alternative guide to understand the needs and experience of family carers of persons living with some form of dementia. Aim of the research Based on transition theory, the aim of our research was to develop an online toolkit to support family carers of persons with some form of dementia and other chronic conditions living in the community. The resulting toolkit, known as the My Tools for Care, is an online self-administered intervention that provides resources and education to support family carers. Approach A mixed methods pragmatic randomized control trial was conducted with 185 participants (from Alberta and Ontario Canada) randomly assigned to a treatment or control group. My Tools for Care was evaluated for its impact on carer self-efficacy, hope, and quality of life. Outcome: My Tools for Care was perceived to help participants to reflect on: their caregiving journey, how far they’ve come, and what supports they have available to them. Participants appreciated that the tool provided information and education for the carer and some noted that the tool helped to reflect on the importance of self-care. Conclusions My Tools for Care is an innovative online intervention that is cost-effective, flexible to the needs of the carer, and offers resources and educational support for family carers of persons living with some form of dementia and other chronic conditions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".