Consent and Inclusion of People Living with Dementia (PLWD) in Research: Establishing a Canadian Agenda for Inclusive Rights-Based Practices
Bibliographic record
Abstract
BACKGROUND: People living with dementia (PLWD) may want to participate in research, but the guidelines and processes enacted across various contexts may prohibit this from happening. OBJECTIVE: Understanding the experiences of people with lived experiences of dementia requires meaningful inclusion in research, as is consistent with rights-based perspectives. Currently, the inclusion of PLWD in Canadian research is complex, and guidelines and conceptual frameworks have not been fully developed. METHODS: This research note outlines a three-year proof-of-concept grant on the inclusion and consent of PLWD in research. FINDINGS: It presents a brief report on some of the contradictions and challenges that exist in legislation, research guidelines, and research practices and raises a series of questions as part of an agenda on rights and inclusion of PLWD in research. DISCUSSION: It suggests conceptual, legal, and policy issues that need to be addressed and invites Canadian researchers to re-envision research practices and to advocate for law and policy reform that enables dementia research to align and respect the rights and personhood of PLWD.
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 | 0.501 | 0.435 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.051 | 0.111 |
| Scholarly communication | 0.031 | 0.025 |
| Open science | 0.011 | 0.044 |
| Research integrity | 0.019 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".