Feasibility, Acceptability, and Impact of a Self-guided e-Learning Memory Program for Older Adults
Bibliographic record
Abstract
Abstract Clinician-led memory interventions have been shown to increase knowledge, reduce anxiety, promote memory-strategy use, and increase brain-healthy lifestyle behaviours in older adults with normal age-related memory changes. A self-guided, e-learning version of the Baycrest Memory and Aging Program® was recently developed to increase accessibility to memory interventions. The objectives of the current study were to assess program feasibility (retention rate), acceptability (satisfaction), and participant-reported impact (memory concerns, behaviour change, goal attainment). As part of a larger study, participants were 139 healthy older adults (mean age: 73±7, 73% female). Ninety-two individuals completed the program (retention rate=66%). Anonymous feedback data indicated a high level of satisfaction with the program overall (98%), the pace and clarity of the learning modules (100%), and the organization and navigation of the interface (92%). Suggested improvements included offering more interaction with others and addressing minor platform glitches. There was a decrease in the level of concern about memory change, with 64% expressing concern at a level consistent with the Jessen et al. (2014) criteria for Subjective Cognitive Decline at baseline, and 23% expressing the same at post-test. The majority of participants reported increases in using memory-strategies (63-97%) and lifestyle-promoting behaviours (40-72%). All participants reported moderate to high satisfaction with personal goal attainment. Results support feasibility, acceptability, and impact of a self-guided e-learning adaptation of memory intervention. E-learning tools may be a promising avenue to deliver accessible brain health promotion in later life, especially in the context of the shift to virtual care during and beyond COVID-19.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".