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 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 | 0.127 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".