Creating National Dementia Caregiver Profiles: A Pilot Study
Bibliographic record
Abstract
Abstract Background There will be an estimated 1 million people living with dementia (PLWD) in Canada in 2031, with a further 613,500 Canadians thrust into the role of caregiver to support them. These unpaid caregivers, who tend to be spouses or adult children, shoulder significant physical, mental, emotional, and financial burdens while caring for their loved ones. Yet there is almost no information available nationally on caregivers of people living with dementia and their needs Method The First Link® program by the Alzheimer Society offers tailored support and connection to caregivers reaching ∼200,000 clients nationally. Consistent quantitative data will be collected from 30,000 individuals across 5 pilot provinces. The pilot recruitment ensures representation and inclusivity of all communities served by the Society. A framework and data set were agreed to through a collaborative community of practice approach engaging provincial Societies, subject matter experts and PLWD. Data sharing agreements were established for automated, secure, de‐identified data transfers into a centralized data repository. Result The framework includes a common dataset with demographic (race, ethnicity, gender identity, sexual identity), health status, level of stress/caregiver burden, confidence/preparedness to deliver care, quality of life and connections to local supports. Through an individualized tailored approach, each pilot site receives training in consistent data collection processes as well as equity, diversity, inclusion and belonging sensitivity training. This approach allowed staggered onboarding while ensuring all front‐line staff are trained for longitudinal data collection with 100% uptake. The next step will be engagement to develop caregiver profiles that highlight demographic characteristics, insights into current supports, and priorities for future programming for this population, and allow for subgroup analysis that provides further information on the different sizes of each provincial site, as well as styles of First Link® delivery, healthcare connectedness, and geography. Conclusion Over time, this comprehensive dataset will provide insight into the effectiveness of caregiver resources that are currently being provided (e.g., public education, community support groups, crisis support), including ease of access for caregivers, and establish a Canada‐wide surveillance system for caregivers of PLWD
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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 | 0.032 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".