Co-Developing CARED: A Personalized Dementia Outcome Measure With Caregivers
Bibliographic record
Abstract
Abstract Background Outcome measures for dementia often fail to reflect the wide variation in symptoms, behaviors, and functional changes experienced by persons living with dementia and their caregivers. Standardized tools may not capture what matters most to patients and families, limiting clinical relevance and applicability. This poster describes the development of the Caregiver-Reported Outcome Measure for Dementia (CARED), a novel tool co-developed by researchers and family caregivers that integrates both standardization and personalization. Methods CARED was developed through a community–academic partnership in which family caregivers served as co-researchers. Caregivers contributed to defining domains, refining items, and making decisions about measure structure. Surveys, co-created with community partners, collected caregiver responses from the US and Canada to identify CARED domains and items. Results The final measure consists of 27 total items, four of which are core items to be completed by all respondents, and 23 additional items from which caregivers select the four most relevant to their situation. The personalized structure of CARED ensures the inclusion of universally important outcomes while accommodating individual caregiving circumstances. The co-development process demonstrates the feasibility and value of embedding caregiver partners as partners in measurement design. Implications CARED represents an innovative approach to dementia outcomes measurement. Through a partnership of researchers and family caregivers, the tool was developed with the lived experiences and perspectives of the population of interest. This framework may guide future efforts to develop outcome measures that are both personalized and standardized, ensuring greater clinical applicability and responsiveness to patient and caregiver needs.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".