Enhancing Minds in Motion® as a virtual program delivery model for people living with dementia and their care partners
Bibliographic record
Abstract
The Alzheimer Society of Ontario's Minds in Motion (MiM) program improves physical function and well-being of people living with dementia (PLWD) and their care partners (CP) (Regan et al., 2019). With the COVID-19 pandemic, there was an urgent need to transition to a virtual MiM that was similarly safe and effective. The purpose of this mixed methods study is to describe the standardized, virtual MiM and evaluate its acceptability, and impact on quality of life, and physical and cognitive activity of participants. Survey of ad hoc virtual MiM practices and a literature review informed the design of the standardized MiM program: 8 weeks of weekly 90-minute sessions that included 45-minutes of physical activity and 45-minutes of cognitive stimulation in each session. Participants completed a standardized, virtual MiM at one of 6 participating Alzheimer Societies in Ontario, as well as assessments of quality of life, physical and cognitive activity, and program satisfaction pre- and post-program. In all, 111 PLWD and 90 CP participated in the evaluation (average age of 74.6±9.4 years, 61.2% had a college/university degree or greater, 80.6% were married, 48.6% of PLWD and 75.6% of CP were women). No adverse events occurred. MiM participants rated the program highly (average score of 4.5/5). PLWD reported improved quality of life post-MiM (p = <0.01). Altogether, participants reported increased physical activity levels (p = <0.01) and cognitive activity levels (p = <0.01). The virtual MiM program is acceptable, safe, and effective at improving quality of life, cognitive and physical activity levels for PLWD, and cognitive and physical activity levels among CP.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".