Errorless (re)learning of daily living routines by a woman with impaired memory and initiation: Transferrable to a new home?
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To use errorless learning to train a memory- and initiation-impaired woman on two activities of daily living routines and then to transfer these routines to a new home. RESEARCH DESIGN: Single case quasi-experimental. METHODS AND PROCEDURES: Over 9 months, a young woman with an anterior cerebral haemorrhagic stroke (secondary to a ruptured arteriovenous malformation) was trained on routines of morning self-care and diabetes management, involving extensive practice on a structured series of steps with intervention as needed to prevent errors. Once routines were established, family members were trained in the supervision and rating of the routines at home. Following discharge, caregivers continued to monitor the routines daily for 3 months. MAIN OUTCOMES: Errorless learning of self-care and diabetes routines was successful. The routines were transferred to a new home environment and maintained at a near perfect level over a 3-month follow-up period. The patient remained severely memory-impaired, indicating that her functional gains were not attributable to any recovery of her memory abilities over time. CONCLUSIONS: This case offers evidence that even people with severe memory and initiation impairments can be trained on new routines using errorless learning and that, once learned, these routines can be carried out in novel contexts.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".