Neuroscience research involving older persons in Canada: some legal and neuroethical concerns.
Bibliographic record
Abstract
In this paper, I will examine some legal and ethical issues that arise in relation to neuroscience research involving older persons in Canada. Such research includes research relating to dementia, including Alzheimer's disease. Dementias such as Alzheimer's are organic diseases affecting mainly older persons. They adversely affect mental acuity. There is still much that is unknown about such illnesses, making research on these diseases particularly necessary. In this paper, I will identify and focus on particular concerns with respect to the participation of older persons in research. These concerns are: Inclusion in and Access to Research; Informed Consent; Incidental Findings; and Advance Directives. I will discuss each of these concerns in the context of Canadian research ethics policy and law. The aim of this paper is not to provide an exhaustive discussion of these issues, each of which may rightfully demand a full paper. The aim of this paper is to identify and paint a canvass of these particularly relevant issues, discuss the policy and law on them, identify any existing gaps and propose some solutions to remedy these gaps and protect older persons who participate in research.
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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 | 0.049 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.034 | 0.025 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.019 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".