COLLABORATIVE RESEARCH, CAPACITY BUILDING AND KNOWLEDGE TRANSLATION DEVELOPMENT FOR RESEARCH ON ADVERSE BIRTH OUTCOMES AND THE ENVIRONMENT
Bibliographic record
Abstract
Abstract BACKGROUND The DoMiNO (Data mining & Neonatal outcomes) project explores the relationship between the environment and adverse birth outcomes. The project applies Integrated Knowledge Translation; a collaborative approach that builds on the participation, expertise and perspectives of interdisciplinary researchers, clinicians and knowledge-users, to ground and enhance the depth and breadth of research and to facilitate knowledge translation. Understanding the components that impact team building processes can contribute to supporting collaborative efforts. OBJECTIVES Based on the DoMiNO project we present major components that contributed to building team capacity for knowledge creation and the development of a knowledge translation plan. DESIGN/METHODS We use this project’s Integrated Knowledge Translation process as a case study in a qualitative evaluation of the ongoing research collaboration (experience and learnings) following team engagement in the research process (e.g., meetings, informal interactions). Participants included all 24 DoMiNO team members. Data were collected through interviews, focus groups, surveys and participant observations, all adding to the cumulative understanding of the collaborative research process and the knowledge translation plan evolution. All data were coded and analyzed using thematic analysis procedures. RESULTS Findings highlight the interrelated components of building capacity to support the research progress, co-production and knowledge translation plan development. These components include commitment, work etiquette, balancing perspectives, power and ownership, as well as communication, transparency, learning/ sharing knowledge and alignment. These contribute to building relationships, trust and capacity. Once those were established and research deliverables were clearer, the main messages and attainable knowledge translation goals were identified. The knowledge translation Several components contribute to capacity building and the development of the KT plan. In this complex context, it is an ongoing iterative process that evolves through time, as the team works and builds capacity. Identifying and supporting the essential components of team development could optimize capacity building. plan was then articulated to identify potential users, audiences, and strategies. CONCLUSION Several components contribute to capacity building and the development of the KT plan. In this complex context, it is an ongoing iterative process that evolves through time, as the team works and builds capacity. Identifying and supporting the essential components of team development could optimize capacity building.
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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 | 0.241 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.006 | 0.042 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".