Improving ways of working with researchers with lived expertise (of homelessness)
Notice bibliographique
Résumé
It has become increasingly apparent that conducting rigorous, relevant and accepted research requires including Researchers with Lived Expertise/Experience (RWLE) in the research process. Including RWLE can create new knowledge rooted in new perspectives, and support research that is more relevant to affected populations. However, the conversation has primarily focused on how involving RWLE can improve research outcomes, so that inequities are being better addressed; there is very little focus on how involvement in research can and should benefit RWLE themselves, nor of how the research process itself can demonstrate a commitment to addressing inequities. In this commentary, we reflect on the experiences of two RWLE of Homelessness, and a Senior Research Associate who all worked together on a recent study. Informed by the challenges we faced and ways we navigated these, here we discuss key issues that must be given more consideration as involving RWLE becomes a necessary part of conducting research. Research teams must consider the issues of pay equity and job security for RLWE who often work on short-term contracts; supporting the professional development of RWLE for their own career advancement; and paying attention to the language we use and how we communicate research findings so they are accessible. There is a unique opportunity for research teams to incorporate a philosophical and practical orientation towards equity during the research process. While research seeks to understand and explain a phenomenon, it must simultaneously seek to address this very phenomenon through how the research is conducted. Our aim is to further the discussion around including RWLE, and to provide tangible suggestions for research organizations and teams. It is widely understood that involving individuals with lived expertise, often called Researchers with Lived Expertise/Experience (RWLE), in research is important. There are clear benefits for the relevance, quality, and acceptance of research findings. Including RWLE can create new knowledge and perspectives, and support research that is more relevant to the populations experiencing issues. However, the conversation has primarily focused on how involvement of RWLE can improve the research outcomes, so that inequities are being better addressed; there is very little focus on how involvement in research should benefit RWLE themselves, nor of the broader need to address inequities through the research process (not just the research outcomes). Research teams must consider the issues of pay equity and job security for RLWE who often work on short-term contracts; supporting the professional development of RWLE for their own career advancement; and paying attention to the language we use and how we communicate research findings so it is accessible. In this commentary, we reflect on the experiences of two RWLE of Homelessness, and a Senior Research Associate who all worked together on a recent study. Through the challenges we faced and ways we navigated these, we discuss key issues that must be given more consideration as involving RWLE is recognized as a necessary part of conducting research.
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 | 0,338 | 0,348 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,005 |
| Bibliométrie | 0,008 | 0,004 |
| Études des sciences et des technologies | 0,046 | 0,088 |
| Communication savante | 0,059 | 0,081 |
| Science ouverte | 0,013 | 0,093 |
| Intégrité de la recherche | 0,021 | 0,036 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,006 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».