THE EFFECTIVENESS OF REIMAGINE AGING: AN INTERVENTION TO REDUCE INTERNALIZED AGEISM IN OLDER ADULTS
Bibliographic record
Abstract
Abstract A life time of exposure to ageist messaging and beliefs may lead older adults to internalize this messaging and direct it inwards, or towards their peers. This internalized ageism can have severe multi-faceted consequences on the well-being of older adults. However, little research has addressed reducing internalized ageism. Thus, Reimagine Aging, a six-week process-based intervention to reduce internalized ageism was designed and implemented, using tools of education, acceptance and commitment therapy, and attributional retraining to target theoretically based mechanisms of change. The data from 72 community-dwelling older adults (M = 70.4 years, SD = 6.4 years) were analyzed for changes in perceptions of aging and mechanisms of change. Participants completed questionnaires prior to the intervention, immediately following the intervention, and at a two-month follow-up. Results demonstrated that participants’ self-perceptions of aging (partial eta squared = 0.37, p < .001) and perceptions of older adults (partial eta squared = 0.27, p < .001) became significantly more positive and maintained these gains at follow-up, associated with large effect sizes. Furthermore, the increases in self-perceptions of aging were mediated by increases in the theory-related mechanisms of psychological flexibility, mindfulness, and perceived control. Positive gains in perceptions of older adults were mediated by these process-based factors to a lesser degree. This study provides initial support for the effectiveness of this process-based intervention targeting a reduction of internalized ageism. This has the potential to reduce the harmful consequences of internalized ageism impacting older adults globally.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".