Errorless learning and spaced retrieval techniques to relearn instrumental activities of daily living in mild Alzheimer’s disease: A case report study
Bibliographic record
Abstract
Stéphanie Thivierge1,2, Martine Simard1,2, Léonie Jean1,2, Éric Grandmaison31School of Psychology, Laval University, Quebec City, Quebec, Canada; 2Centre de Recherche Université Laval Robert-Giffard, Quebec City, Quebec, Canada; 3Neurosciences and Rehabilitation Units, Moncton Hospital, Moncton, NB, CanadaAbstract: Previous studies on cognitive training in Alzheimer’s disease (AD) were principally aimed at making patients learn items not related to functional needs. However, AD patients also experience difficulties with instrumental activities of daily living (IADL). The goal of the present multiple baseline case report study was to assess the preliminary efficacy and tolerability of an individualized cognitive training program using the errorless learning (EL) and spaced-retrieval (SR) techniques to relearn forgotten IADLs in mild AD. Following an exhaustive neuropsychological assessment, two participants received two training sessions per week during four weeks. Participant A was trained to use his voice mail and Participant B, to manage the messages from his answering machine. The results showed that the program was well tolerated and improved performance on the trained tasks. These ameliorations were maintained over a 5-week period. The effects of the training did not have any impact on global cognitive functions since the results on these measures remained relatively stable. This case report demonstrated preliminary efficacy of a new cognitive training program using EL and SR techniques tailored to the needs of AD patients. This is an important finding since the loss of these capacities alters autonomy in AD patients.Keywords: Alzheimer’s disease, errorless learning, spaced retrieval, memory, IADLs
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".