Case Reports in the Integration of Technology with Cognitive Rehabilitation for Individuals with Memory Concerns and Their Care Partners
Bibliographic record
Abstract
OBJECTIVE: Technology can be combined with psychological interventions to support older adults with memory concerns. Using a bi-phasic design, cognitive rehabilitation (CR) was integrated with off-the-shelf technology and delivered to two people with cognitive impairment, and one care partner. METHOD: Pre- and post-intervention assessments were completed for all participants. Individuals with memory problems received pre- and post-intervention remote neuropsychological assessment (i.e., Rey auditory verbal learning test; mental alternations test; animal fluency), and the hospital anxiety and depression scale (HADS). The care partner completed the HADS, Zarit burden interview, and neuropsychiatric inventory questionnaire. Change metrics incorporated reliable change indices where possible. Goals were tracked using the Canadian occupation performance measure; these data were analyzed through visual inspection. A research journal (used to document intervention process) was analyzed thematically. RESULTS: Results cautiously suggested our integration was feasible and acceptable across several technologies and varying goals. Across participants, significant changes in goal progress suggested the integration of technology with CR successfully facilitated goal performance and satisfaction. The research journal underscored the importance of a visual component, intervention flexibility, and a strong therapeutic alliance in integrating technology and CR. CONCLUSIONS: CR and technology present a promising avenue for supporting people living with cognitive impairment. Further exploration of technology and CR with a range of etiologies and target goals is warranted.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".