Knowledge Translation and Implementation Planning to Promote Research Governance in Nongovernment Organizations in the Torres Strait: Descriptive Study
Bibliographic record
Abstract
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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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 | 0.031 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".