HEALTH SURVEILLANCE OF PERSONS LIVING WITH DEMENTIA AND CAREGIVER DYADS
Bibliographic record
Abstract
Abstract The relationship between individuals living with dementia and their caregivers is important and can impact their dementia journey. However, limited national longitudinal data exists about the caregiver-person living with dementia dyad. Availability of such data would provide important information about joint trajectories and help to better identify the needs of caregivers and persons living with dementia across the dementia journey. The objective of this study was to develop a linked national longitudinal database of persons living with dementia and their caregivers. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) extracts and de-identifies clinical data from electronic medical records (EMR) from approximately 2 million patients across Canada. CPCSSN data is used to identify persons living with dementia and caregivers willing to participate in the study. CPCSSN data from participating dyads are linked (e.g., chronic and mental health conditions, diagnoses, laboratory test results) and additional information about the experiences of persons living with dementia and their caregivers (e.g., ethnicity, amount and type of care provided, burden, availability of support) are collected yearly using surveys. The growing database contains linked, de-identified, comprehensive information about persons living with dementia and their caregivers that will become a rich source of data for researchers, clinicians, and policymakers. Specifics around how the database was developed, and lessons learned will be discussed as these findings can be used as a template to develop similar linked health surveillance databases.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".