Facilitating stakeholder engagement in early stage translational research
Bibliographic record
Abstract
INTRODUCTION: Stakeholder engagement can play an important role in increasing public trust and the understanding of scientific research and its impact. Frameworks for stakeholder identification exist, but these frameworks may not apply well to basic science and early stage translational research. METHODS: Four Clinical and Translational Science Award (CTSA) hubs led six focus groups and two semi-structured interviews using a semi-structured discussion guide to learn from basic science researchers about stakeholder engagement in their work. The 24 participants represented fourteen clinical and academic disciplines. RESULTS: Early stage translational researchers reported engagement with a broad array of stakeholders. Those whose research has a clinical focus reported working with a more diverse range of stakeholders than those whose work did not. Common barriers to stakeholder engagement were grouped into three major themes: a poor definition of concepts, absence of guidance, and limited resources. DISCUSSION: The National Center for Advancing Translational Sciences (NCATS), the consortium of CTSAs, and the individual CTSA "hubs" are three actors that can help early stage translational researchers develop shared terms of reference, build the necessary skills, and assemble the appropriate resources for engaging stakeholders in Clinical and Translational Research. Getting this right will involve a coordinated push by all three entities.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.267 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.043 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".