Digitising the wisdom of our elders: connectedness through digital storytelling
Bibliographic record
Abstract
Abstract Digital storytelling provides older adults with an opportunity to become digital producers, connect with others through story and explore their life history. The authors report on the results of a digital storytelling project for older adults. The study investigated the experiences and perceived benefits of older adults who created digital stories during a ten-week course and explored the reactions of story viewers to the digital stories they viewed during a special sharing event. Eighty-eight older adult participants in Metro Vancouver who attended one of 13 courses offered were included in the study. Most of the participants were female and over half were immigrants. Results from the focus group interviews demonstrated a rich array of reported social and emotional benefits experienced through the process of creating a digital story within the course. Three main themes emerged: social connectedness through shared experience and story, reminiscence and reflecting on life, and creating a legacy. Viewers who attended a ‘Sharing Our Stories’ event reported that the stories were meaningful, well constructed and invoked a range of emotions. The researchers conclude that digital storytelling may help digital storytellers increase connectedness to others and to self. Additionally, this connectedness may extend over time through the process of examining the past to create a digital story that can serve as a legacy to connect to future generations.
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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 | 0.010 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".