COMPAs—An app designed to support communication between persons living with dementia in long‐term care and their caregivers
Bibliographic record
Abstract
Abstract Background People with dementia experience autonomy loss, and require caregiver support on a daily basis. Dementia is also characterized by progressive communication disorders, leading to isolation in both persons with dementia and caregivers. Caregivers also experience stress and increasing burden which makes them particularly susceptible to burnout. As a whole, these factors hamper quality of life in both members of the dyad. The present study examines the efficacy of COMPAs, an App designed on principles of person‐centered and emotional communication, with the purpose of improving well‐being in persons with dementia and their caregivers, including the extreme isolation conditions imposed by COVID‐19. Method In this implementation study on COMPAs, caregivers in two long‐term care facilities were trained to use COMPAs and taught strategies to improve communication with persons with dementia. Measures were taken before and after 8 weeks of interventions with COMPAs. Qualitative analyses on semi‐structured interviews and quantitative analysis on questionnaires were completed. Results Interventions with COMPAs improved the quality of communication and quality of life within the dyad. They also reduced caregiver burden and increased their feeling of personal accomplishment. Conclusion COMPAs contributes to the well‐being of persons with dementia and their caregivers, thanks to its focus on emotional communication. It represents a valid tool to improve person‐centered communication in long‐term care facilities, including extreme isolation contexts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".