Sharing Stories as Legacy: What Matters to Older Adults?
Bibliographic record
Abstract
Objectives: Legacy allows individuals to make meaning of their lives by passing on their experiences and beliefs to younger people and influencing their perspectives, perceptions, and actions. This mixed-methods study investigated: (1) What is important for older adults to share as legacy with families, friends and others, based on the types and features of their digital stories ? and (2) How do older adults’ digital stories affect story viewers? Methods: One hundred adults aged between 55 and 95 years participated in ten-week Elder’s Digital Storytelling courses and created short digital stories. Using the content analysis approach, the story transcripts were thematically analyzed and iteratively coded by three researchers and the results were quantified. A diverse group of 60 viewers at a public event provided their reactions to the digital stories. Results: The findings revealed that character, place, and family were chosen as the primary types by the older adults for their legacy digital stories. Accomplishment and career/school were the next most prominent story types. Moreover, these digital stories appeared to have a powerful impact on the viewers. Discussion: A digital story is a powerful artifact to communicate an older person’s legacy because it is based on familiar forms of communication, such as speech and photographs. The major legacy themes chosen by the older adults align with the findings of the research literature. The feedback from the viewers of the digital stories reflects these as a source of life wisdom and legacy for younger generations. Funding details: This work was supported by the AGE-WELL National Centre of Excellence (AW CRP 2015-WP4.3).
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| 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".