A new frontier in spaced retrieval memory training for persons with Alzheimer's disease
Bibliographic record
Abstract
The objective of this pilot study was to investigate how a memory training technique called "Spaced Retrieval" (SR) might be effectively applied in helping persons with Alzheimer's disease improve their recall of recent events. Capitalising on the interdependence among spared and impaired memory systems, it was hypothesised that SR training with support at encoding and retrieval would facilitate the retention and recall of meaningful recent events. Eight participants with a diagnosis of Alzheimer's disease or related disorder were recruited for this study. The study employed a quasi-experimental multiple baseline treatment design across participants, items, and behaviours. SR training was provided in three domains: Semantic, Prospective, and Episodic recent memory. The results show important training gains made by all participants across conditions at post-training follow-up. In the Episodic condition, participants were able to recall specific details about recent events following training. This study provides preliminary evidence that individuals with mild to severe cognitive impairment can learn and recall new episodic information through Spaced Retrieval training. If replicated, these findings would support the use of Spaced Retrieval as an intervention tool to help individuals maintain their functioning in the area of episodic recent memory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".