An Evidence-Based Systematic Review on Cognitive Interventions for Individuals With Dementia
Bibliographic record
Abstract
PURPOSE: To evaluate the current state of research evidence related to cognitive interventions for individuals with Alzheimer's disease or related dementias. METHOD: A systematic search of the literature was conducted across 27 electronic databases based on a set of a priori questions, inclusion/exclusion criteria, and search parameters. Studies were appraised for methodological quality and categorized according to intervention technique and outcome (e.g., cognitive-communication impairment or activity limitation/participation restriction). Results were summarized and, when possible, analyzed quantitatively using indicators of treatment effect size. RESULTS: Forty-three studies met criteria for inclusion in the review. The most commonly used cognitive intervention techniques used were errorless learning, spaced-retrieval training, vanishing cues, or verbal instruction/cueing. Most treatment outcomes were measured at the cognitive-communication impairment level of functioning and were generally positive. However, results should be interpreted cautiously because of methodological limitations across studies. CONCLUSIONS: Research evidence to support the use of cognitive interventions for individuals with dementia is accumulating. Researchers are beginning to evaluate treatment efficacy, yet the focus tends to be on discovery, specifically, refining intervention variables that will facilitate optimal outcomes. Implications for clinical practice and avenues for future research 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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".