Overcoming the Epistemic Injustices Experienced by Older Adults in Health Innovations: How and What Can This Lead to?
Bibliographic record
Abstract
Abstract Epistemic injustices experienced by older adults refer to the marginalization of their knowledge, experiences, and perspectives, which are often ignored or devalued in social, cultural, and political discourses and decisions. This leads to limited inclusion of their perspectives when health innovations concerning them are developed, including those designed to support their social participation. This presentation aims to discuss a research project conducted with the goal of: 1) identifying factors associated with epistemic injustices when including the knowledge of older adults in the development of health innovations, and 2) implementing actions to counter these factors to co-create, with various societal actors, including older adults and their loved ones, a health innovation that places them at the center of medical and support interactions concerning them. This project was carried out using a living lab method, which is a participatory and collaborative research approach where participants (n = 40), from local communities, were actively involved in co-constructing knowledge and practical solutions. The results highlighted actors contributing to epistemic injustices were identified, including organizational disharmony and internalized ageism. The principles and values of a philosophical research community, and steps proposed by the Occupational Justice Collaborative Framework were used to address these factors. Multiple iterative working meetings with a co-development committee and an advisory committee led to the development of an innovative technological tool that enhances the agency and strengthens older adults’ social participation by fostering greater inclusion and engagement in decision-making processes that affect their lives.
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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 | 0.041 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".