Effects of Spaced Retrieval Training on Semantic Memory in Alzheimer's Disease: A Systematic Review
Bibliographic record
Abstract
PURPOSE: This article reports on a systematic review and meta-analysis of the effects of spaced retrieval training (SRT) on semantic memory in people with Alzheimer’s disease (AD) or related disorder. METHOD: An initial systematic database search identified 454 potential studies. After screening and de-duplication, 35 studies that used SRT with the population of interest remained. The authors used an appraisal point system to evaluate the quality of the studies. Twelve of the 35 studies met inclusion and exclusion criteria and passed the appraisal point system cutoff. The 12 studies were classified as Level I and II evidence. RESULTS: Although the 12 studies varied in terms of design, methodology, and quality, SRT was shown to have important positive effects on learning semantic information across the included studies. CONCLUSIONS: The findings indicate that SRT is an effective semantic memory training technique for people with AD, and consequently, recommendations are suggested for implementing SRT in practice settings. Continued research in this domain is also warranted to address limitations and gaps in the current body of research evidence, including variability in SRT protocols, effects of dementia severity on learning outcomes, maintenance effects, generalization, and the role of explicit and implicit learning in SRT.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".