Knowledge Translation and Implementation Planning to Promote Research Governance in Nongovernment Organizations in the Torres Strait: Descriptive Study
Notice bibliographique
Résumé
BACKGROUND: Aboriginal and Torres Strait Islander people in Australia have participated in Western research for decades. When done well, research has resulted in significant benefits and positive impacts on society. However, the primary benefactor of this research has and continues to be researchers, with limited or no research knowledge mobilized for uptake and beneficial use by end users, such as individuals and communities. In 2021, the Torres Strait Islanders Research to Policy and Practice Hub (the Hub) at James Cook University designed and implemented several strategies, including a games-based interactive workshop with representatives from nongovernment organizations (NGOs). Feedback suggests the workshop and associated activities were a success. OBJECTIVE: We describe knowledge translation (KT) and implementation planning to design and implement strategies to increase awareness and understanding of NGOs in research governance. METHODS: This descriptive study involved representatives from NGOs on Thursday Island in the Torres Strait. We collected data from a literature review and informal discussions. We used several models and frameworks to guide our approach and underpin data collection and analysis. RESULTS: Designing and implementing strategies to increase awareness and understanding of NGOs in the Torres Strait to govern research involved several key steps: (1) identifying and defining what needed to change and who needed to change, (2) identifying and mapping barriers and facilitators, (3) selecting the most appropriate strategies to support change, (4) implementing activities, and (5) monitoring and evaluating our approach. We developed a program logic to understand and communicate to others how we would implement activities and what resources would be required to support this process. We drew on several evidence-based KT and implementation models and frameworks to do this. First, a KT planning template was used to inform what evidence we wanted to mobilize, to whom, and for what purpose. Based on this step, we recognized we wanted to bring about change with the target audience, and as such, we drew on the previously mentioned implementation planning models and frameworks. We collated the outcomes from these initial steps. CONCLUSIONS: Our KT and implementation practice experience were successful. Encouraging researchers and end users to adopt similar practices requires investment in training and development of KT and implementation practice. This also entails modifying research standards and guidelines to include KT and implementation practice when working with Aboriginal and Torres Strait Islander people and other vulnerable groups, creating incentives for researchers and end users to embed KT and implementation practice in research, and recognizing and rewarding the benefits and impact beyond publication and presentation.
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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 | 0,031 | 0,057 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».