CREATING DIGITAL STORIES ON AGING EXPERIENCES: A RAPID METHODOLOGICAL REVIEW OF DEVELOPMENT PROCEDURES
Bibliographic record
Abstract
Abstract Digital storytelling is increasingly used to showcase the life histories of older adults and their communities. The Mobilizing Aging in Place (MAP) project team was tasked to engage older adults in the creation of five digital stories as a means of sharing the findings of two studies on aging in place with municipal and regional planners and health care policy/decisionmakers. While there were numerous examples of digital stories on aging experiences, the best way to develop digital stories with older adults was unclear. To create clarity in effective methods, we conducted a rapid methodological review of existing literature on digital storytelling involving older adults and experiences of aging. After searching in the Discovery search engine, we reviewed 61 abstracts and articles based on specific inclusion and exclusion criteria. This yielded 18 articles that we included in the review. The included literature discussed digital stories on various health and intergenerational knowledge transfer topics. Researchers used a variety of software, editing tools, and recording methods to combine images, videos, and sounds. Digital stories were developed both in collaboration with older adults and by the older adult participants themselves. The review showed that there are no set criteria or best practices for creating digital stories, and that workshops and interviews are commonly used to engage older adults in digital storytelling. We recommend a more comprehensive and broader review be undertaken with a focus on all forms of digital storytelling regardless of type of population and context.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.233 | 0.369 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.062 | 0.037 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".