Design requirements for a digital storytelling application for people with mild cognitive impairment (MCI)
Bibliographic record
Abstract
Background The current digital storytelling applications present advantages for individuals with Mild Cognitive Impairment (MCI); however, there exists a notable oversight regarding their potential to facilitate group-based storytelling activities with this population. This study endeavors to identify design requirements for a more inclusive and accessible digital storytelling tool for people with MCI. Method The methodological framework encompasses distinct stages, commencing with focus groups and interviews (Stage 1), followed by prototyping workshops (Stage 2) and qualitative prototype testing (Stage 3). The comprehensive three-stage research involved participants residing in Beijing, China, including 43 people with MCI aged 65–95 years ( M = 79.09, SD = 8.99), with a mean Montreal Cognitive Assessment score of 21.91 (range = 18–26, SD = 2.40). Additionally, 17 care partners and 10 occupational or clinical therapists actively participated. Result The culmination of the three-stage research process has yielded 12 discernible key design requirements. Preferred storytelling themes center around narratives designed to elicit positive emotions. The narrative material generation process involves a systematic approach, unlocking memories through carefully formulated questions. In memory retrieval, users are provided with hints, bolstering confidence and perpetuating a semblance of face-to-face interaction. The focus in story sharing lies in transcending mere narration and extending it to a wider audience. Conclusion This case study centers on crafting a digital storytelling application to enhance social connections for people with MCI. It delves into crucial design requirements addressing memory challenges, emphasizing individual preparation and group sharing. The developed digital storytelling application demonstrates potential to offer valuable memory support and foster personal and collective connections. Future research will focus on formal testing to evaluate these outcomes.
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 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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".