Using a Multisectoral Approach to Advance Health Equity in Rural Arizona: Community-Engaged Survey Development and Implementation Study
Bibliographic record
Abstract
BACKGROUND: Over the past decade, public health research and practice sectors have shifted their focus away from identifying health disparities and toward addressing the social, environmental, and economic determinants of health equity. Given the complex and interrelated nature of these determinants, developing policies that will advance health equity requires collaboration across sectors outside of health. However, engaging various stakeholder groups, tapping into their unique knowledge systems, and identifying common objectives across sectors is difficult and time consuming and can impede collaborative efforts. OBJECTIVE: The Southwest Health Equity Research Collaborative at Northern Arizona University, in partnership with an 11-member community advisory council, is addressing this need with a joint community-campus effort to develop and implement a Regional Health Equity Survey (RHES) designed to generate an interdisciplinary body of knowledge, which will be used to guide future multisectoral action for improving community health and well-being. METHODS: Researchers and community partners used facilitated discussions and free listing techniques to generate survey items. The community partners pilot tested the survey instrument to evaluate its feasibility and duration before survey administration. Respondent-driven sampling was used to ensure that participants included leadership from across all sectors and regions of northern Arizona. RESULTS: Over the course of 6 months, 206 participants representing 13 sectors across the 5 counties of northern Arizona were recruited to participate in an RHES. Survey response rates, completion percentage, and sector representation were used to assess the effectiveness and feasibility of using a community-engaged apporach for survey development and participant recruitment. The findings describe the current capacity to impact health equity by using a multisectoral approach in northern Arizona. CONCLUSIONS: The Southwest Health Equity Research Collaborative effectively engaged community members to assist with the development and implementation of an RHES aimed at understanding and promoting multisectoral action on the root causes of health inequity. The results will help to build research and evaluation capacity to address the social, economic, and environmental conditions of health inequity in the region.
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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 | 0.028 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".