Perspective Chapter: Research Ethics and Older Adults as Research Participants – What Needs to Change?
Bibliographic record
Abstract
In this chapter, we explore the ways in which we can better understand how university-based ethic review committees, and the protocols associated with research that include older adults, both help and hinder research, and how decisions can be shaped by and contribute towards narratives of ageism. Conceptions of what it means to age are rooted in historic biomedical ideas about the body, in juxtaposition to a richer understanding of the lifespan, history and diversity, intersectionality, and social determinants of health. This chapter explores how decisions made within ethic review committees in universities may be seen to protect older adults from unethical research practices and associated harms, and though well-intentioned, contribute towards the reproduction of ageist discourses and what it means to grow older, to be vulnerable, and to be in need of protection. This chapter draws insights gained from twenty years of research in multi-national, provincial, and local teams, teaching all levels of aging related courses at a local university, and work in the community. This research has been located in Canada where the Tri-Council Policy Guidelines require all research that includes human subjects to be approved a priori through a local research ethic review committee.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".