Cultural adaptation in television technology for older adults with dementia in care settings
Bibliographic record
Abstract
Introduction: There is a lack of literature on the cultural adaptation of technologies for dementia care. This paper presents an example of the cultural adaptation of a television video about drinking water for older Chinese adults with dementia in care settings, in Vancouver, Canada. Method: We refer to the cultural adaptation process (CAP) model to guide the cultural adaptation process by collecting and incorporating feedback from different parties into the culturally adapted video, including Phase 1 local consultations and information gathering, phase 2 iterative testing and adaptation, and phase 3 finalizing adaptation. Results: We also referred to the Ecological Validity Model (EVM) to present the adjustments we made to the video from the cultural adaptation. We adjusted the video on seven domains suggested by the EVM: goal, context, content, language, people, concept, and method. Discussion: We draw attention to the opportunities and challenges of the cultural adaptation of technology into a new community. Based on our lessons, we outline concrete suggestions about what aspects of, and how, cultural adaptation can be made to promote cultural inclusivity in technology development and implementation.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".