How can equity, diversity, and inclusion (EDI) principles be incorporated into research excellence with industry and community partners? Lessons learnt from Canada andAustralia
Bibliographic record
Abstract
Abstract Background The rapid advancement of gerontechnology, technologies for older adults, needs a collaborative approach, integrating the efforts of researchers, industry and community partners. Multisectoral collaboration fosters a holistic view, merging industry expertise, academic rigour, and the lived experiences of older adults and caregivers. This paper explores the role of Equity, Diversity, and Inclusion (EDI) perspectives in such collaboration. Methods We present two case studies from Canada and Australia. Study one involves a Dementia Television project, and study two an innovative rural dementia care project. Data sources included case studies’ focus group transcripts, research meeting notes, or publications between 2021–2023 and 2016–2024, respectively. Utilizing Rofel’s reflective model, we reflected on lessons learned regarding challenges, strategies, and their implications for future research. Our analysis focused on two questions: 1) What were the common challenges we encountered in partnering with industry and community in the research process? And 2) How can EDI be applied to help overcome those challenges? Results Thematic analysis identified five common themes of challenges and ten practical strategies. The challenges are (1) implicit bias, (2) underrepresentation, (3) communication gaps, (4) mistrust and (5) power dynamics. Based on the lessons learned, we identified ten practical strategies through EDI principles: (1) diverse representation, (2) establish transparent agreements, (3) inclusive language and cultural sensitivity, (4) apply flexibility to learn and adapt, (5) embed team reflection (6) take time to build trust and relationships, (7) meaningful engagement, (8) equitable recognition (9) foster a respectful environment for shared learning, and (10) cultivate a long-term sustained relationship. Conclusion The older population is diverse, and their needs are complex. EDI considerations contribute to fostering research excellence and maximizing the potential to develop aging technologies that truly meet the diverse needs of older adults for societal impact. Multisector collaboration requires clear communication and intentional efforts to build trust. EDI considerations should be embedded at every stage of the research process. This paper outlined common challenges, strategies, and implications as practical tips for future research and practice.
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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 | 0.128 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.043 | 0.048 |
| Scholarly communication | 0.032 | 0.019 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.007 | 0.012 |
| 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".