Effectiveness of a Process Based Intervention in Decreasing Internalized Ageism
Bibliographic record
Abstract
Objectives: Exposure to ageism may be internalized in older adults, and this can have severe consequences. 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 education, acceptance and commitment therapy, and attributional retraining to target theoretically based mechanisms of change. Method: 72 older adults (M = 70.4 years, SD = 6.4 years) participated in Reimagine Aging, consented to participate in this research, and provided valid data. Participants completed questionnaires prior to the intervention, immediately following the intervention, and at a two-month follow-up. Results: Participants’ self-perceptions of aging and perceptions of older adults became significantly more positive, associated with large effect sizes (partial eta squared = 0.37 and 0.27 respectively). Furthermore, these positive gains were mediated by increases in psychological flexibility, mindfulness, and perceived control. Discussion: 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".