Community‐Engaged Strategies to Promote Relevance of Research Capacity‐Building Efforts Targeting Community Organizations
Bibliographic record
Abstract
OBJECTIVE: The study goal is to highlight strategies for promoting relevance of research capacity-building efforts targeting community organizations (CO)s. METHODS: Two community partners, representing two COs, were invited to participate in CO research development trainings, Community Research Forums (Forum)s. Their contributions were documented via Forum document review. Forum participants, representatives from other COs, completed post-Forum surveys to identify additional training needs and rate Forum impact relative to their training expectations. A content-based analysis and descriptive statistics were used to summarize needs assessment- and impact-related survey responses, respectively. RESULTS: Community partners were involved in eight Forum-related activities including marketing (planning), facilitation (implementation), and manuscript coauthorship (dissemination). Eighty-one individuals, representing 55 COs, attended the Forums. Needs assessment responses revealed a desire for additional assistance with existing Forum topics (e.g., defining research priorities) and a need for new ones (e.g., promoting organizational buy in for research). Ninety-one percent of participants agreed that the Forum demonstrated the value of research to COs and how to create a research agenda. CONCLUSIONS: Including community partners in all Forum phases ensured that CO perspectives were integrated throughout. Post-Forum needs and impact assessment results will help in tailoring, where needed, future training topics and strategies, respectively.
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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 | 0.166 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".