A Participatory Trust-Building Model for Conducting Health Equity Research With Rural and Urban Native American, Black, and Latinx Communities: WEAVE NM (Wide Engagement for Assessing Vaccine Equity in New Mexico)
Bibliographic record
Abstract
A Participatory Trust-Building Model for Conducting Health Equity Research With Rural and Urban Native American, Black, and Latinx Communities: WEAVE NM (Wide Engagement for Assessing Vaccine Equity in New Mexico) Lisa Cacari Stone PhD, Anabel Canchola BS, Elroy Keetso MS, Enrique López-Escalera MSW, Cathryn McGill BS, Linda Son-Stone PhD, Susie Villalobos EdD, Daniel Shattuck PhD, Carlos Linares MD, MPH, Nathania Tsosie MPH, Vincent Werito PhD, Tassy Parker RN, PhD, and Nina Wallerstein DrPH Affiliation Lisa Cacari Stone and Carlos Linares are with the Transdisciplinary Research, Equity, and Engagement Center, University of New Mexico, Albuquerque. Anabel Canchola is with the Dona Ana County Department of Health and Human Services, Las Cruces, NM. Elroy Keetso is a Tribal relations specialist with the Albuquerque, New Mexico, and Tri-Chapter Area, Navajo Nation. Enrique López-Escalera is a private social work practitioner, Las Cruces. Cathryn McGill is with the New Mexico Black Leadership Council, Albuquerque. Linda Son-Stone is with First Nations Community HealthSource, Albuquerque. Susie Villalobos is with the National Latino Behavioral Health Association, Cochiti Lake, NM. Daniel Shattuck is with the Pacific Institute for Research and Evaluation, Las Cruces. Nathania Tsosie and Tassy Parker are with the Center for Native American Health and the Department of Family and Community Medicine, School of Medicine, University of New Mexico. Vincent Werito is with the College of Education, University of New Mexico. Nina Wallerstein is with the Center for Participatory Research University of New Mexico. CopyRightCorrespondence should be sent to Lisa Cacari Stone, Professor, College of Population Health, Transdisciplinary Research, Equity and Engagement Center, University of New Mexico Health Sciences Center, 1011 Las Lomas Rd NE, Albuquerque, NM 87102 (e-mail: lcacari-stone@salud.unm.edu). Reprints can be ordered at http://www.ajph.org by clicking the "Reprints" link. CONTRIBUTORS L. Cacari Stone, A. Canchola, E. Keetso, E. López-Escalera, C. McGill, L. Son-Stone, S. Villalobos, D. Shattuck, N. Tsosie, and V. Werito drafted the editorial and the community trust-building vignettes, reviewed and provided comments for the trust-building figure, and reviewed the final version of the editorial. L. Cacari Stone and C. Linares developed the trust-building conceptual figure. A. Canchola, C. McGill, L. Son-Stone, N. Tsosie, V. Werito, and T. Parker interpreted the trust-building model and provided detailed review and editing. N. Wallerstein provided review and editing. https://doi.org/10.2105/AJPH.2023.307469 Accepted: September 24, 2023 Published Online: November 09, 2023
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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 | 0.135 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".