COMPAS: AN APP TO PROMOTE PERSON-CENTERED COMMUNICATION BETWEEN PEOPLE LIVING WITH DEMENTIA AND THEIR CAREGIVERS
Bibliographic record
Abstract
Abstract Introduction People living with dementia (PWD) experience communication deficits as soon as the early stages of the diseases, deficits which significantly increase over time. In the last stages, constant communication breakdowns lead to reduced exchanges with caregivers, resulting in the isolation of both communication partners. These difficulties have negative impacts on the quality of life of PWD and their caregivers, who themselves face increasing burden. While communication difficulties in PWD are a core issue in care, few interventions to address this issue have been developed. Aims: The present study focused on COMPAs, an app designed to sustain communication between PWD and their caregivers. COMPAS has been shown to trigger emotional communication during the short co-viewing of personalized audiovisual material. Method: This was a pre-post intervention study with COMPAs in 2 long-term care centers. Seventeen caregivers used COMPAs in the context of daily routines over eight weeks with seventeen PWD. Data collection included specific questionnaires and semi-structured interviews to measure effects on communication and caregiver burden. Data analyses combined quantitative and qualitative approaches. Results In caregivers, there was a significant improvement in personal achievement at work. Semi-structured interviews showed an improvement in communication in the dyad and a more empathetic approach to caregiving. Discussion These results indicate that the communication triggered by COMPAs breaks down communication barriers, by creating positive exchanges through personalized emotionally driven exchanges, while stimulating empathy and personalized interventions. Caregivers see COMPAs as an ecological tool to address communication barriers, while facilitating an empathetic caregiving relationship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".