How can equity, diversity, and inclusion (EDI) principles be incorporated into research excellence with industry and community partners? Lessons learned from Canada and Australia on projects with a dementia focus
Bibliographic record
Abstract
The rapid advancement of gerontechnology, technologies for older adults, needs a collaboration that integrates the efforts of researchers, industry and community partners. Multisector collaboration fosters a holistic view of technologies, 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 Patient and Public Involvement (PPI). We present two case studies from Canada and Australia. Study One involves a dementia television project, and Study Two is an innovative rural dementia care project. Data sources included transcripts of the case studies’ focus groups, research meeting notes, and associated study publications between 2021 and 2023 and 2016–2024, respectively. Utilizing Rolfe’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 of partnering with industry and PPI in the research process? And (2) How can EDI be applied to help overcome those challenges? Thematic analysis identified five common themes of challenges and ten practical strategies. The challenges are (1) experiential bias, (2) underrepresentation, (3) communication gaps, (4) mistrust and (5) power dynamics. Based on the lessons learned, we identified ten practical strategies using EDI principles: (1) seek diverse representation, (2) establish transparent agreements, (3) adopt 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) facilitate meaningful engagement, (8) provide equitable recognition and opportunity, (9) foster a respectful environment for knowledge transfer, and (10) cultivate a long-term sustained relationship. The older population is diverse, and their needs are complex. EDI considerations contribute to fostering research excellence and maximizing the potential of PPI to develop technologies to improve aging experiences 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 outlines common challenges, strategies, and implications as practical tips for future research and practice. The development of technologies for older adults needs a collaboration that integrates the efforts of researchers, industry and community partners. This paper explores the role of Equity, Diversity, and Inclusion (EDI) perspectives in Patient and Public Involvement (PPI). We present two case studies from Canada and Australia. Study One involves a dementia television project, and Study Two is an innovative rural dementia care project. 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 of partnering with industry and PPI in the research process? And 2) How can EDI be applied to help overcome those challenges? We 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 using EDI principles: (1) recruit for diverse representation, (2) establish transparent agreements, (3) incorporate 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) facilitate meaningful engagement, (8) ensure equitable recognition (9) foster a respectful environment for shared learning, and (10) cultivate a long-term sustained relationship. This paper outlines common challenges, strategies, and implications as practical tips for future research and practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.047 |
| Research integrity | 0.001 | 0.020 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".