DIGITAL STORYTELLING IN INTERVENTIONS WITH OLDER ADULTS—WHAT DOES THE LITERATURE SAY?
Bibliographic record
Abstract
The aim of this literature review was to explore how has digital storytelling been used among older adults with typical aging, with dementia, and with cognitive impairment in interventions? We searched eight databases for studies that investigated the use of digital storytelling in older adults. Four researchers independently applied selection criteria and extracted data from the selected papers. One researcher with a broad experience in gerontology and research acted as a third reviewer to solve conflicts. Covidence software was used to manage the literature review. A total of 3366 references were retrieved from the databases, 13 duplicates were removed. After that 231 references are being analyzed by the team. Preliminary results show that digital storytelling is used to promote social engagement and reminiscence in older adults; to preserve traditions; and, to facilitate connectedness between elderly and young people including students. Some of the technologies used are multimedia that combine family photographs, film clips, audio narration, and music along with documentary video-making. Themes in the stories include older adults’ lives, experiences living with diseases, and factors that contribute to longevity. Positive changes in older adults are confidence, level of speech, sense of purpose and fellowship, social engagement, motivation and, intent to change one’s health behavior. Most of the studies used a qualitative approach. The literature on digital storytelling for older adults is in its early stages and the use of artificial intelligence is promising for facilitating the construction of the digital stories with 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 | 0.015 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".