Relational community engagement within health interventions at varied outcome scales
Bibliographic record
Abstract
Relational community engagement may be a powerful approach with multiple health outcomes. Relational community engagement has the potential to promote health and involves collaborative efforts between multiple stakeholders. The COVID-19 pandemic further highlighted the centrality of community engagement in health crises. Challenges continue to persist, however, in genuinely engaging and empowering communities for better health outcomes. Understanding the multi-level and complex relational nature of community engagement is essential to comprehend its influence on health at micro, meso, and macro scales of influence. The purpose of this narrative review was to synthesize the literature on relational community engagement within varied health interventions at the three major system levels (micro, meso, and macro) to support the development of future research agendas. At the micro level, relational community engagement interventions demonstrated a range of positive outcomes including: increased sense of control, satisfaction, positive behavior, improved knowledge, behavior change, empowerment, and overall positive health and social outcomes. At the meso level, relational community engagement interventions resulted in increased trust between stakeholders and groups/teams, and increased community senses of ownership of interventions, decisions, structures. At the macro level, relational community engagement interventions influenced broader societal factors and had positive impacts on health policy and governance including collaboration between sectors and communities as well as increased access to services. The review highlights the potential versatility and effectiveness of interventions that prioritize relationships, health promotion, and social change while underscoring the significance of holistic and community-centered approaches in addressing diverse health and social challenges.
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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 | 0.044 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".