SUccessful Caregiver Communication and Everyday Situation Support in dementia care (SUCCESS): Using technology to support caregivers
Bibliographic record
Abstract
Abstract Background Caregivers of people with dementia (PwD) are diverse—they live in urban and rural locations, have varying educational backgrounds, different access to resources, different relationships to the care recipient, etc. However, one common feature of all caregivers is the need to have access to education and support on their caregiving journey. Technology provides a feasible way to reach such a large and diverse population. SUccessful Caregiver Communication and Everyday Situation Support in dementia care (SUCCESS) provides an innovative training and coaching application on a mobile device (an App) to support formal and informal caregivers of PwD. Through the use of an affective avatar, the SUCCESS App aims to help caregivers better understand dementia and related behaviours and learn communication methods grounded on the principle that opinions and needs of PwD should be acknowledged, respected, and addressed. The avatar also assists caregivers in focussing on the remaining abilities of the PwD; thereby, helping to support PwDs’ continued engagement in meaningful activities. Finally, the avatar acts as a wellness coach to help ensure caregivers remain as resilient as possible by encouraging caregivers to actively engage in individualized self‐care activities. Methods The SUCCESS App was evaluated in a pilot study in two countries using a pre‐post design. Participants included 63 carers of PwD located in Austria (n=26) and Romania (n=37). Quantitative and qualitative data was collected at baseline, 1‐month, 3‐months and 6‐ months. The primary outcome was satisfaction with care. Secondary outcomes included care‐related knowledge, experience of burden, and the presentation of behavioural and psychological symptoms of dementia by the PwD. Results Preliminary analysis of the qualitative data demonstrates the majority of the participants found the application to be very useful and practical; they also indicated they planned to continue to use it once the trial was over because it helped them to have a better understanding of what dementia is and assisted them in tangible ways as they provided care to PwD. Conclusions Initial results indicate that the SUCCESS App has the potential to provide information and support to caregivers in a highly accessible way that is both useful and feasible.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".