Development, feasibility, and acceptability of a process based intervention to decrease internalized ageism
Bibliographic record
Abstract
A lifetime of exposure to ageism may be internalized in older adults, and these ageist beliefs that are directed inwards can have severe consequences. However, research on reducing internalized ageism is scarce. To address this, we designed and implemented a six-week online process-based intervention to reduce internalized ageism and to assess its feasibility. The intervention utilized a process-based therapy approach targeting psychological, behavioral, and physiological pathways through which internalized ageism negatively impacts health, as specified by stereotype embodiment theory. Intervention components included education, acceptance and commitment therapy techniques, and attributional retraining. A total of 81 older adult participants participated in the feasibility study. Most participants rated each session and the overall program as very useful after each session (average program usefulness rating of 4.54/5). Participants also attributed a wide range of novel behaviors to this intervention and stated that they felt it changed their perspectives on ageism and/or internalized ageism. Results from this study provide a promising foundation from which to advance research on interventions that address internalized ageism - a problem that has severe consequences on the health and well-being of growing numbers of 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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".