Comprehensive strategy for capturing and integrating community input into community research training curricula
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Community stakeholders often participate in community research training curricula development. There is limited information describing how their input informs curricula. This paper describes input solicitation methods, input received, and examples of its integration. METHODS: From June 2014 to June 2016, community members (CMs) and community-based organizations (CBOs) guided curricula development tailored for CMs and CBOs, respectively. Engagement methods included a strategic planning retreat, surveys, a listening session, workgroup meetings, and community engagement studios. Descriptive statistics were used to summarize survey input. For other methods, input was extracted and compiled from facilitator notes. RESULTS: CMs (n = 37) and CBOs (n = 83) providing input included patients and caregivers and advocacy, community service, and faith-based organizations, respectively. The major feedback categories were training topic priorities, format (e.g., face-to-face vs. online), logistics (e.g., training frequency), and compensation (e.g., appro-priateness). Input directly guided design of CBO and CM curricula (e.g., additional time devoted to specific topics based on feedback) or helped to finalize logistics. CONCLUSIONS: Multiple quantitative and qualitative methods can be used to elicit input from community stakeholders to inform the development of community research training curricula. This input is essential for the development of training curricula that are culturally relevant and acceptable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it