PERSONS WITH DEMENTIA USE DIGITAL STORYTELLING TO ENHANCE MEMORY, CONNECT SOCIALLY, LEAVE LEGACIES
Bibliographic record
Abstract
We used WeVideo, an online video editing platform to collaborate with people living with dementia to create digital stories. Three cohorts of participants with varying degrees of cognitive impairment were recruited: 6 in Vancouver, 7 in Edmonton, and 7 in Toronto. Over six to eight weeks, researchers met with participants individually to develop their stories and to input photos, voice over, sound effects, music, and video. In cases where no personal photographs were available, researchers acquired freely available images from the Internet that illustrated the participant’s narratives, for example street scenes or sports teams from a certain era. Each participant was invited to share their completed digital story with their care partners and families. The digital stories covered themes of personal accounts of war, family, travel, employment, hobbies and advocacy for the dementia community. The digital stories evoked joy and sadness, and shared reminiscing. For some, the digital stories were an engaging way to share meaningful stories and socially connect with children, grandchildren, and great grandchildren. Several women chose to create stories about families, perhaps to leave legacies and messages for future generations. Some participants commented that the process required drawing on memories and thinking about events they had not contemplated for years. Some could remember more about their past than they thought. Participant recruitment and digital storytelling processes varied slightly across the three sites to accommodate different participant needs and organizational preferences. The project provides insights into best practices for facilitating digital storytelling for persons with dementia.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".