"Let's Connect": Implementing a tablet‐based intervention for people with dementia and caregivers
Bibliographic record
Abstract
Abstract Background Let’s Connect training to use tablets and accessible apps with people living with dementia was implemented with staff, volunteers and family caregivers to evaluate the program impact. Method Eighty‐six staff and volunteers (18‐88 years of age) in 14 adult day programs and long‐term care homes in Ontario, Canada received training to deliver Let’s Connect groups as part of their daily or weekly programming. They were supported to deliver Let’s Connect group program for an initial eight sessions program with 118 people with dementia (56‐98 years of age). Staff and volunteers completed the Dementia Attitudes Scale (DAS) before and after the eight sessions and an exit interview, which was also completed by family caregivers who received training to support Let's Connect at home. People with dementia completed the QoL‐AD and SPS‐10 measure of social connectedness plus exit interviews. Result There was an improvement in staff and volunteer’s DAS score, while the people with dementia attending the adult day programs maintained their relatively high levels of social connectedness. Thematic analysis identified social interaction as a key benefit for staff, volunteers, caregivers and people with dementia. Further benefits and potential barriers were also identified. Conclusion Let’s Connect was well‐received, directly improved attitudes towards dementia and enhanced social activity. All of the sites that received training continued with the Let’s Connect group program after the research project ended. We will share our training strategy that can be applied to other tablet‐based activities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".