Changes in narratives about aging among older adults in an internalized ageism intervention
Bibliographic record
Abstract
OBJECTIVES: Exposure to ageism over a lifetime may be internalized in older adults and cause negative health outcomes. However, little research has sought to understand how to reduce internalized ageism. This study explored aging narratives among older adults prior to and following their engagement in a six-week 'Reimagine Aging' intervention. This intervention employed education and acceptance-and-commitment therapy (ACT) to reduce internalized ageism. METHOD: We utilized content analysis to understand shifts in the aging narratives of 75 older adults pre- and post-intervention. We employed inductive content analysis to identify categories at Time 1 and Time 2 and directed content analysis to evaluate within-participant changes in aging narratives. RESULTS: The ways in which participants wrote about their aging selves appeared to be less influenced by internalized ageism post-intervention. Time 1 narratives focused on worries about the future and on-going challenges related to health, loss, and decline. Time 2 narratives shifted towards an emphasis on engagement in meaningful activities and adaptive coping. CONCLUSION: Our previous research demonstrated that the Reimagine Aging intervention reduced internalized ageism. The current study adds to these findings, demonstrating qualitative shifts in how participants described their aging selves, replacing categories that focus on ageism and loss with positive and values-focused categories.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".