Interactive CO-learning for Research Engagement and Education (I-COREE) Curriculum to Build Capacity Between Community Partners and Academic Researchers
Bibliographic record
Abstract
The voice of diverse communities continues to be minimal in academic research. Few models exist for education and training of new research topics and terminology and building partnership capacity in community-engaged research. Little is known about integrative education and training when building participatory research partnerships for sustainability and developing trust and rapport. Community partners at an Asian community-based health and social services center in a large metropolitan area wanted to explore the cultural context of a health-assistive smart home that monitors and auto-alerts with changes in health. With historical and recent rising trends in culturally insensitive research in several diverse communities, the concept of technology-enabled monitoring in the privacy of one's home brings uncertainty. Academic nurse researchers and community partners co-created a culturally safe integrative education and training curriculum, the Interactive CO-learning for Research Engagement and Education (I-COREE). The purpose was to design, implement, and evaluate the curriculum to respond to the community partners' needs to create a culturally safe space through an integrative education and training to facilitate building partnership capacity for research engagement including developing trust and rapport and addressing uncertainties in health-assistive technologies. Popular education tenets informed the curriculum. Twelve academic and community partners participated, four were team teachers who co-led the session. Implementation of the experiential, multimodal co-learning activities were conducted within ahalf-day. The curriculum evaluation indicated that it helped bridge critical conversations about partners' fears of the unknown, approach culturally sensitive topics safely, and trust and rapport. Key elements may be translatable to other partnerships.
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How this classification was reachedexpand
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 | 0.025 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".