RLIFE™: AN ONLINE PLATFORM TO SUPPORT THE SOCIAL INTERACTIONS OF INDIVIDUALS WITH DEMENTIA
Bibliographic record
Abstract
Social connectedness and participation in meaningful activities can help individuals with dementia maintain a sense of belonging, stay independent at home longer, slow cognitive decline, increase life satisfaction, and promote aging in place. However, there is a lack of consolidated resources to support individuals with dementia with the fulfillment of their social needs after a dementia diagnosis. There are some websites and online information boards to support individuals with dementia; however, the amount of information can be overwhelming because it is not adapted to their needs. The demand to increase and facilitate social connectedness for individuals with dementia encourages the development of solutions tailored to this population. The objective of this work is to develop a prototype of an online platform to Reconnect individuals with mild to moderate dementia to Life, through social Interaction and Fulfilling Experiences (rLifeTM). Using a transdisciplinary approach, a prototype was co-created based on the information provided by different stakeholders (i.e., individuals with dementia, clinicians, technicians, engineers, family caregivers, and researchers). We first identified areas for social interaction and translated them into the conception of a matching algorithm aimed at connecting individuals with dementia to personalized opportunities tailored to their needs, values and preferences. We then defined a non-profit model of operation that utilizes partnerships with dementia-focused organizations to ensure the sustainability of the platform. Future directions include developing the services offered on the platform, and designing and testing the user interface with individuals in our target population.
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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 | 0.000 |
| 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".