The effects of modified spaced-retrieval training on learning and retention of face–name associations by individuals with dementia
Bibliographic record
Abstract
The purpose of this project was to assess the effects of spaced-retrieval training (SRT) on learning of new and previously known associations by individuals with dementia in two treatment conditions: one in which the recall intervals were filled with activities unrelated to the information being learned (unrelated condition) and one in which the intervals were filled with related activities (related condition). Thirty-two individuals with mild to moderate dementia (30 with a diagnosis of Alzheimer's disease; two with vascular dementia) participated in the study. On average, participants learned the associations in fewer than four sessions and retained the information for variable amounts of time, up to 6 weeks. Previously known associations were learned significantly faster than new associations. The modified SRT format, in which the within-session recall intervals were filled with information related to the target association, did not result in faster learning or longer retention of learned associations. Participants learned previously known associations in the standard SRT format (with unrelated information in the recall intervals) significantly faster than new associations taught in the modified SRT condition. Cognitive impairment, as measured by the Mini-Mental State Examination, was significantly correlated with time to learn new associations, but did not explain a large proportion of the variance in new learning. Theoretical and clinical implications are discussed.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".