MINDFUL AGING: THE IMPACT OF TRAIT MINDFULNESS ON AGING STEREOTYPES
Bibliographic record
Abstract
With a growing aging population, it is important to understand factors that encourage or discourage healthy aging processes as we get older. Previous research has shown that negative beliefs about aging (i.e., negative aging stereotypes) can negatively affect health behaviours, memory performance and physical function. Thus, discovering ways to decrease negative aging stereotypes may aid in promoting healthy aging. It is postulated that mindfulness may attenuate negative aging stereotypes, as mindfulness cultivates openness, curiosity, and non-judgment. In the present study, we assessed whether mindfulness was associated with fewer negative beliefs and opinions about aging. Participants (N = 201) aged 55+ completed the Five Facet Mindfulness Questionnaire (FFMQ) and the Expectations Regarding Aging Survey (ERA-38) as part of an online study examining the psychological correlates of health behaviour in middle-aged and older adults. Controlling for age, sex, education, and retirement status, multiple regression analyses show that enhanced trait mindfulness is significantly associated with more positive beliefs about aging and expectations regarding cognitive function, mental health, sleep, and appearance (all ps <.05). These data suggest that interventions aimed at changing attitudes and beliefs, such as mindfulness-based interventions, may reduce negative aging stereotypes and improve quality of life among older adults.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".