The design and implementation of culturally-safe dementia risk reduction strategies for immigrant women: a theoretical review
Bibliographic record
Abstract
BACKGROUND: Raising awareness about dementia risk reduction is particularly important for ethno-culturally diverse or immigrant women, who have greater risk of dementia compared with men due to multiple interacting factors. We aimed to synthesize prior research on culturally-safe strategies to raise diverse women's awareness of dementia risk reduction. METHODS: We conducted a theoretical review. We searched for studies published up to April 2023 included in a prior review and multiple databases. We screened studies and extracted data in triplicate, informed by existing and compiled theoretical frameworks (WIDER, RE-AIM, cultural safety approaches) and used summary statistics, tables and text to report study characteristics, and strategy design, cultural tailoring, implementation and impact. RESULTS: We included 17 studies published from 2006 to 2021. Most were conducted in the United States (15, 88%), before-after cohorts (7, 41%), and included African, Caribbean or Latin Americans (82%). No studies focused solely on women (median women 72%, range 50% to 95%). All strategies consisted of in-person didactic lectures, supplemented with interactive discussion, role-playing, videos and/or reinforcing material. Strategies varied widely in terms of format, delivery, personnel, and length, frequency and duration. Details about tailoring for cultural safety were brief and varied across studies. Ten approaches were used to tailor strategies, most often, use of target participants' first language. Assessment of implementation was limited to reach and effectiveness, offering little insight on how to promote adoption, fidelity of implementation and longer-term maintenance of strategies. Strategies increased knowledge of dementia and decreased misconceptions, but did not prompt participants to seek dementia screening in the single study that assessed behaviour. CONCLUSIONS: While this review revealed a paucity of research, it offers insight on how to design culturally-safe dementia risk reduction strategies that may be suitable for ethno-culturally diverse or immigrant women. Healthcare professionals can use these findings to inform policy, clinical guidelines and public health programs. Future research is needed to establish the ideal number, length and duration of sessions, and confirm strategy effectiveness for diverse women.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".