Reducing Gerontophobia and Improving Cognition: A Study on the Efficacy of Mindfulness‐Based Cognitive Therapy in Older Adults in India
Bibliographic record
Abstract
BACKGROUND: Gerontophobia, characterized by anxiety about aging, negatively affects mental and cognitive health in older adults. In India, where aging is often stigmatized, effective interventions are needed. Mindfulness-Based Cognitive Therapy (MBCT) has shown promise in reducing anxiety and improving cognition. This study examines the impact of MBCT on gerontophobia and cognitive functioning in non-demented older adults aged 50-60 years. METHODS: A quasi-experimental pre-test/post-test design was employed with 35 participants divided into two groups based on Anxiety Aging Scale (AAS) scores: gerontophobia group (AAS ≥ 10, n = 19) and non-gerontophobia group (AAS < 10, n = 16). Both groups underwent an 2-week MBCT program. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Paired t-tests and ANCOVA analyzed within and between-group differences. RESULTS: MBCT significantly reduced gerontophobia in the gerontophobia group (AAS: Pre M = 14.6, SD = 3.1; post M = 9.8, SD = 2.7; t(18) = -6.32, p < 0.001) and improved cognition (MoCA: Pre M = 24.1, SD = 3.4; post M = 26.5, SD = 3.1; t(18) = 6.58, p < 0.001). Among sub-domains, visuospatial skills (Gerontophobia group: t(18) = 6.12, p < 0.001), executive functions (Gerontophobia group: t(18) = 5.28, p < 0.001), and attention (Gerontophobia group: t(18) = 4.76, p < 0.001) demonstrated statistically significant pre-post improvement. CONCLUSION: MBCT effectively reduces aging-related anxiety and enhances cognition in older adults, demonstrating its potential as a relevant intervention in India.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".