A self-guided e-learning program improves metamemory outcomes in healthy older adults: a randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: Aging brings memory changes that can be concerning for some older adults. Whereas in-person memory interventions can positively impact knowledge, mental health, and behavioural outcomes, self-guided e-learning programs may offer scalable and accessible alternatives to in-person programming. The current study aimed to evaluate efficacy of an e-learning program compared to no treatment. METHOD: The trial was registered at ClinicalTrials.gov (NCT03602768). As part of a larger, multi-arm, controlled trial, healthy older adults (ages 60-84, 71% female) were randomized into an intervention or a delayed-start control condition. Data collection personnel were masked to participant grouping. Outcome measures were completed through telephone interviews and online questionnaires at baseline, immediate post-intervention, and 6- to 8-week follow-up. RESULTS: Among 115 analyzed participants, there were larger improvements over time in memory knowledge, memory strategy acquisition and daily use, and self-reported memory satisfaction and ability in the group that completed the intervention than in the control group. There was no interaction effect for health-promoting behaviors. Intention-to-treat analyses showed attenuated but largely similar findings. CONCLUSION: This self-guided e-learning memory program demonstrated similar clinical outcomes provided by in-person, facilitator-led programs. It may serve as an effective first-line treatment for older adults presenting with memory concerns in clinical settings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".