The dementia journey survey: A community engagement approach for inclusivity in dementia care research
Bibliographic record
Abstract
Abstract Background Approximately 560,000 people live with dementia (PLWD) in Canada, yet research on the experiences of ethnoculturally diverse people with dementia is largely unknown. As a result, healthcare programs and services do not reflect the dementia related needs of these communities. Additionally, 60% of primary care physicians indicated feeling uncomfortable supporting patients with dementia, despite being the first point of access for additional dementia healthcare. To address these gaps, a national collaboration was developed by the Alzheimer Society of Canada (ASC) and the College of Family Physicians of Canada (CFPC) to understand more diverse experiences of PLWD with their healthcare providers. Method Using a mixed methods design, paper and online surveys were created for PLWD and caregivers, and online surveys were created for family physicians. CFPC registered family physicians were invited to participate through professional channels. PLWD, and caregivers were recruited through Alzheimer Society provincial chapters and community partners. To ensure inclusivity of underrepresented populations, a targeted community engagement strategy was developed to connect with members of various ethnocultural groups across Canada using a multi‐tiered selection process of cultural community‐based organizations. Qualitative analysis will include thematic analysis using a standard process to code data and generate themes. Quantitative analysis will use descriptive statistics. Result This survey is the first of its kind in Canada that investigates the relationships between PLWD and caregivers with their primary health providers and utilizes a defined and consistent approach to community outreach and engagement. The survey bridges the knowledge gap in dementia care research, focusing on inclusivity of ethnocultural communities which has resulted in increased uptake of the survey and will provide richer data for resource creation. Conclusion Findings from the survey will inform the ASC‐CFPC collaboration in creating resources to increase physician comfort with managing all patients with dementia. It will also guide the development of new resources and educational materials for PLWD and caregivers. The community engagement strategy will help create and strengthen community relationships with ethnoculturally diverse communities through a mutual knowledge sharing and community building.
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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 | 0.047 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".