Mindfulness-based interventions for older adults with late-life anxiety
Bibliographic record
Abstract
Anxiety in older adults can be debilitating and affect every aspect of daily life including cognitive function, sleep, and stress management. Although the presentation of anxiety can vary, common symptoms reported to primary care providers are insomnia, agitation, and irritation. The prevalence of anxiety in Canada is reported at 6.4% affecting about 2.5 million people across the lifespan. In 2012, anxiety costed the Canadian healthcare system an estimated $119.8 million per 100,000 people, and this cost is expected to increase with the rapidly aging population. The use of pharmacotherapy for the first line treatment of anxiety in older adults is common, has limited effectiveness and produce side effects such as increased risk of falls leading to hospitalization. Older adults have expressed interest in receiving mindfulness-based interventions for anxiety as an alternative or complementary treatment. The overall purpose of this dissertation was to expand knowledge related to mindfulness-based group treatments for late-life anxiety. Two studies were conducted as part of this dissertation. The first was a systematic review of existing mindfulness-based interventions for late-life anxiety. The second study was a randomized control trial to test the feasibility and acceptability of Emotion-Focused Mindfulness Therapy (EFMT), a mindfulness-based intervention, in older adult populations with anxiety. The key findings indicated the feasibility and acceptability of EFMT delivered through video conferencing for older adults with anxiety. Although the trial was not planned to be powered to examine clinical changes, statistically significant reductions to anxiety were observed in the intervention group post intervention compared to the control group. There was also a statistically significant reduction in the use of memory strategies post-intervention. The findings from this thesis will inform further development and evaluation of mindfulness-based interventions to meaningfully impact the mental health care of older adults with late-life anxiety. Thus, the present dissertation contributes to an emerging body of international research and clinical practice supporting alternative interventions for late-life anxiety in primary care, and also contributes to laying the groundwork for future larger scale studies of EFMT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".