Culturally safe dementia care: Building nursing capacity to care for First Nation Elders with memory loss
Bibliographic record
Abstract
BACKGROUND: Nursing staff require culturally relevant and dementia-specific education to care for the increasing number of First Nation Elders experiencing memory loss. The culturally safe dementia care (CSDC) research team, composed of researchers, decision makers and Secwepemc Elders, was formed to address this. OBJECTIVES: To increase the capacity of nurses to care for First Nations Elders with memory loss in a culturally safe way. METHODS: Our community-based research used purposive sampling and mixed methods to create, implement and evaluate an education programme for nurses. Thirty-four Elders from six Secwepemc communities participated in roundtables to share views and stories of dementia and nursing care. These data were used to create four teaching stories for the storytelling sessions and talking circles with Elders which, together with the Indigenous Cultural Competency (ICC) training, comprised the CSDC education programme. Thirty-eight nurses (healthcare aides, licensed practical nurses and registered nurses) working in Central British Columbia began the CSDC study and 15 nurses took part in the education programme, evaluated the storytelling session and completed the three pre- and post-measures (Approaches to Dementia Questionnaire (ADQ), ICC knowledge quiz and self-assessment, and Care Plans). The pre- and post-tests were scored, and the data were analysed statistically. The data from the roundtables and talking circles were analysed thematically through a collaborative process. RESULTS: The scores for the ADQ Hope sub-scale, the ICC knowledge quiz and the Care Plans increased from pre- to post-test. All nurse participants judged the storytelling session to be effective and their learning outcomes reflected culturally safe dementia care. CONCLUSIONS: This programme can improve the knowledge, skills and values of nurses to provide culturally safe dementia care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".