Cultural Adaptations of Evidence-Based Interventions in Dementia Care: A Critical Review of Literature
Bibliographic record
Abstract
Cultural adaptation is essential for ensuring that interventions are relevant and effective across diverse sociocultural contexts. However, little is known about how these adaptations are conducted and whether they follow structured, evidence-based frameworks. This review critically evaluated how dementia-related interventions have been culturally adapted and assessed the extent to which these adaptations align with the three-step process outlined in the Integrated Strategy for Cultural adaptation of Evidence-Based Interventions. A total of 19 cultural adaptations reported in 23 publications were identified from PubMed, PsycINFO, CINAHL, and Scopus. Of the included interventions, six targeted behavioral and psychological symptoms of dementia, one addressed both people with dementia and their caregivers, and the remainder focused on informal caregivers. All adaptations were guided by structural frameworks and aligned mostly with the integrated strategy for cultural adaptation. Common adaptation processes included stakeholder engagement through focus group, pilot testing, and review and refinement. However, communities were not engaged in intervention selection, a critical gap in ensuring cultural relevance. Although many studies preserved core components, post-adaptation fidelity checks were not conducted in some of the studies. Future adaptations should prioritize stakeholder engagement in intervention selection and incorporate fidelity assessments to maintain both cultural fit and intervention integrity.
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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 | 0.056 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".