Employing Dissonance-Based Interventions to Promote Health Equity Utilizing a Community-Based Participatory Research Approach and Social Network Analysis
Bibliographic record
Abstract
The purpose of this paper is to examine and advocate for the consideration of relevant approaches that can be utilized to increase the effectiveness of cognitive dissonance-based interventions (DBIs) designed to promote health equity. Although DBIs informed by different paradigms have been reported to be effective in creating behavior change, particularly among at-risk populations, their long-term impacts on behavior change have apparently been difficult to sustain. We argue that a community-based participatory research (CBPR) approach could considerably improve the effectiveness and long-term impacts of DBIs by harnessing community strengths, increasing stakeholder participation, and facilitating collaborations and partnerships in the planning, implementation, and evaluation of such interventions. Then, we argue that the benefits of employing a CBPR approach in DBIs can be further enhanced when combined with an approach that intentionally utilizes Social Network Analysis (SNA). SNA applies powerful techniques to recognize the type of connections that hold a specific network together and identify that network’s key and influential stakeholders. We conclude by providing recommendations for the use of CBPR and SNA in DBIs and demonstrating the benefits of our recommendations, especially in the context of promoting health equity.
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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 | 0.035 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.035 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".