Evaluating the impact of a network of research partnerships: a longitudinal multiple case study protocol
Bibliographic record
Abstract
BACKGROUND: Conducting and/or disseminating research together with community stakeholders (e.g. policy-makers, practitioners, community organisations, patients) is a promising approach to generating relevant and impactful research. However, creating strong and successful partnerships between researchers and stakeholders is complex. Thus far, an in-depth understanding of how, when and why these research partnerships are successful is lacking. The aim of this study is to evaluate and explain the outcomes and impacts of a national network of researchers and community stakeholders over time in order to gain a better understanding of how, when and why research partnerships are successful (or not). METHODS: This longitudinal multiple case study will use data from the Canadian Disability Participation Project, a large national network of researchers and community stakeholders working together to enhance community participation among people with physical disabilities. To maximise the impact of research conducted within the Canadian Disability Participation Project network, researchers are supported in developing and implementing knowledge translation plans. The components of the RE-AIM framework (reach, effectiveness, adoption, implementation and maintenance) will guide this study. Data will be collected from different perspectives (researchers, stakeholders) using different methods (logs, surveys, timeline interviews) at different time points during the years 2018-2021. A combination of data analysis methods, including network analysis and cluster analysis, will be used to study the RE-AIM components. Qualitative data will be used to supplement the findings and further understand the variation in the RE-AIM components over time and across groups. DISCUSSION: The outcomes, impacts and processes of conducting and disseminating research together with community stakeholders will be extensively studied. The longitudinal design of this study will provide a unique opportunity to examine research partnerships over time and understand the underlying processes using a variety of innovative research methods (e.g. network analyses, timeline interviews). This study will contribute to opening the 'black box' of doing successful and impactful health research in partnership with community stakeholders. TRIAL REGISTRATION: Open Science Framework: https://osf.io/kj5xa/ .
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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 | 0.228 | 0.130 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".