New learning in dementia: Transfer and spontaneous use of learning in everyday life functioning. Two case studies
Bibliographic record
Abstract
The purpose of these two case studies was to explore the effectiveness of learning methods in dementia when applied in real-life settings and the integration of new skills in daily life functioning. The first participant, DD, learned to look at a calendar with the spaced retrieval method to answer his repeated questions about the current date and calls made to family. Progressive cuing was used by his wife to increase spontaneous use of the calendar, but DD had difficulty integrating the calendar into his routine. The second patient, MD, relearned a leisure activity (listening to music on a cassette radio) and how to participate in a social activity (saying the rosary in a group) with a combination of learning methods. Transfer of these skills in similar contexts was difficult for MD. She never integrated the cassette radio into her daily life routine but she went regularly to the rosary activity, which was cued by an alarm clock. In sum, the learning methods used were very effective with these patients but transfer and spontaneous use were difficult. Since these aspects are essential to rehabilitation, they should be further explored in order to increase the effectiveness of cognitive interventions.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".