Mindfulness and cognitive training interventions that address intersecting cognitive and aging needs of older adults
Bibliographic record
Abstract
Summary Mindfulness and cognitive training interventions are promising models to address impacts (e.g., anxiety and stress) of cognitive impairment among older adults. Combining strategies may yield better outcomes than models offered in isolation. However, there are numerous uncertainties about these interventions, potential for combination, and implementation. Social workers are well placed to offer these interventions. Findings From an initial search of 3,538 records, 13 studies were included in the final review. Mindfulness studies focused on stress reduction or cognitive behavioral therapy. Cognitive training studies applied stimulation or activity approaches. Results indicate that the field is still emerging, as most studies were pilot or feasibility trials. A combination of mindfulness-based stress reduction and brain training activities may offer the most promising model for older adults with cognitive impairment, based on outcome assessments and other factors. A common limitation among the reports was detailed on engaging older adults with cognitive challenges in the design and implementation of these interventions. Applications This realist review deepens the understanding of how, why, for whom, and in what circumstances a combination of mindfulness and cognitive training could be most successful for social workers to address intersecting cognitive and aging needs of older adults. Building evidence on combining mindfulness-based stress reduction and brain training activities among older adults with cognitive impairment could yield promising results, and this review identifies implementation considerations. The review also found a need for psychometric scale development on the benefits of brain training activities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".