The perceived impacts of using dementia-friendly videos to support the psychosocial needs of people with moderate to severe dementia in care settings: A qualitative exploratory study
Bibliographic record
Abstract
Objective: This exploratory qualitative study examines the perceived impacts of using dementia-friendly videos as a digital assistive technology to support the psychosocial needs of people with moderate to severe dementia in care settings. Methods: The study was conducted in a hospital and a long-term care home in Vancouver, Canada. Data were primarily collected through interviews and focus groups with 19 healthcare providers, supplemented by observational interviews with 15 patients/residents. The person-centred care approach guided the analysis. Findings: Seven key themes emerged: (a) providing comfort, (b) connecting with the person's interests and backgrounds, (c) building relationships, (d) promoting engagement and interaction, (e) facilitating activities of daily living, (f) having a sense of community, and (g) facilitating the impacts using appropriate technological equipment. Conclusion: The themes reflected the five psychosocial needs suggested by the person-centred care framework: comfort, identity, attachment, occupation, and inclusion. Our findings extend this framework by highlighting the importance of cultural connection as an integral part of identity to enhance the impact of the videos. Additionally, this study highlights the significance of using appropriate technological equipment to amplify the impact of the videos, which may require organizational support. This study sheds light on research and practice for further development and use of dementia-friendly videos in care settings.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".