The Aging Self: A Narrative Analysis on Generational Lesson Sharing and Embodiment Expressions of Older Adults
Bibliographic record
Abstract
This qualitative study captures individual aging experiences, gaining insight into how older adults understand aging bodies and express age. Three main research questions were developed to respond to that inquiry: (1) How do older adults embody aging? (2) What life lessons are embedded in these expressions of age? And (3) What life lessons are beneficial for younger generations to know for their own aging experiences? The study employed a secondary data analysis of semi-structured interviews, initially completed without restrictions on age, identity, or experience for participant inclusion. Results were analyzed using NVivo software with a constructive narrative analysis focus. Narratives were grouped into three categories based on the time frame central to the interview: past, present, or future. Emerging narratives included being a student, worker, partner in marriage, parent, retiree, and immigrant. The findings demonstrate how different embodiment experiences emerge through reflective narrative construction and influenced lessons shared. These conclusions contribute to understanding how choices and experiences at different stages in the life course can influence the aging experience and how it is projected. Ultimately, the findings emphasize the role that the self and body hold on identity and self-expression for older adults.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".