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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,164 | 0,125 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,045 | 0,053 |
| Communication savante | 0,027 | 0,021 |
| Science ouverte | 0,007 | 0,032 |
| Intégrité de la recherche | 0,007 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».