AN EVALUATION OF REIMAGINE AGING: A NEW THEORY-BASED PROGRAM TO REDUCE INTERNALIZED AGEISM
Bibliographic record
Abstract
Abstract Over the lifespan individuals may internalize ageist beliefs and as they enter older age, direct them towards themselves. This internalized ageism has many deleterious effects. Despite this, few theory-based interventions have attempted to decrease internalized ageism. As such, a six-week online program was developed including education, acceptance and commitment therapy, and attributional retraining to target mechanisms of change (psychological flexibility, mindfulness, perceived control, and empowerment). The six 90-minute sessions consisted of recorded videos, and discussion groups, with during and between-session activities also being part of the program. To evaluate the feasibility of this intervention, a sub-sample of 81 program participants (58 – 85 years old, 92% female) were sent an online questionnaire following each session. Each session received between 77 and 80 responses. Results were overwhelmingly positive. On a scale of 1 (not very useful) to 5 (very useful), roughly two-thirds (65%) rated the program as a whole very useful. Items participants felt the most important to learn included ageism information, acceptance and commitment therapy tools, and reimagining what it means to age well. Participant’s opinion on what they liked most about the program varied. Among others, common aspects identified were the informational videos, the activities, and the discussion groups. Roughly 81% of participants indicated that they completed the between-session activities, and 79% completed bonus activities. The vast majority indicated the program changed their views on ageism and/or internalized ageism. Going forward, we will evaluate this program’s ability to decrease internalized ageism, and the processes by which it achieves this.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".