SKILLS AND STRATEGIES OF FACILITATORS WHO CO-CREATE DIGITAL STORIES WITH PERSONS WITH DEMENTIA
Bibliographic record
Abstract
Abstract Digital storytelling combines storytelling and digital tools to create brief video clips in which narrative, images, and music are embedded, in order to share personal stories. Digital storytelling facilitators can be health and social care providers as well as care partners who collaborate with persons living with dementia to co-create their stories. These facilitators elicit people’s stories and use the technology to create the digital story. Despite their important role, there is a paucity of information on facilitators’ specific skills and strategies used in working with persons living with dementia. The purpose of this project was to examine skills and strategies used by facilitators who co-create digital stories with persons living with mild dementia. Audio recordings of 70 digital storytelling co-creation sessions conducted in three Canadian cities (Edmonton, Vancouver, Toronto) were transcribed and subjected to qualitative content analysis. Regardless of their disciplinary background, facilitators acted as weavers, bringing together narrative threads to co-construct a digital story with participants. Essential communication skills and strategies included active listening, strategic questioning, being comfortable with silence, and therapeutic responding. Building relationships and collaboration were achieved through flexibility, empathy, and encouraging autonomy. To be an effective facilitator of the digital storytelling process with older adults living with dementia, facilitators must adapt their communication strategies and relational skills to the strengths and needs of the older adults with whom they are collaborating.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".