Pathways to Culturally Safe Dementia Care (CSDC): A Concept Analysis and Transformational Model for Practitioners and Organizations
Bibliographic record
Abstract
Culturally safe dementia care (CSDC) remains inconsistently defined, which makes it difficult for health professionals and researchers to operationalize in practice. To clarify CSDC, we conducted a concept analysis using Rodgers' evolutionary method. Eighteen records from diverse cultural groups and care settings were included. Prominent surrogate terms were cultural sensitivity, cultural competency, and cultural appropriateness, while common related terms were person-centred care, compassionate care, and holistic care. Antecedents included provider awareness through self-reflection and commitment to continuous learning, as well as structural, organizational support. Core attributes of CSDC focused on provider qualities such as demonstrating respect, building trust, using culturally responsive communication, and applying holistic and strengths-based approaches; other attributes pointed to organizational responsibilities like creating affirming care environments, providing information, and honouring cultural preferences. Consequences consisted of reduced social isolation and fear of discrimination, and improved trust, care experiences, and quality of life. This analysis emphasizes that CSDC requires ongoing reflection, meaningful engagement, and system-wide accountability. Importantly, CSDC must be co-created with families and communities and should be rooted in their knowledge systems, histories, and priorities. To support application, we share a conceptual model and four model cases to illustrate CSDC in both urban and rural healthcare settings. The model offers a practical pathway that can guide care providers and organizations in implementing culturally safe approaches. Clearer operational frameworks and community-led strategies are needed to move from intention to sustained, culturally grounded practice.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".