Picturing Participation: Catalyzing Conversations About Community Engagement in HIV Community–Based Organizations
Bibliographic record
Abstract
Community engagement is considered a cornerstone of health promotion practice. Yet engagement is a fuzzy term signifying a range of practices. Health scholarship has focused primarily on individual effects of engagement. To understand the complexities of engagement, organizations must also consider relational, structural, and/or organizational factors that inform stakeholders' subjective understandings and experiences. Community engagement processes are not neutral; they can reproduce and/or dismantle power structures, often in contradictory or unexpected ways. This article discusses diverse stakeholders' subjective experiences and understandings of engagement within the HIV sector in Toronto, Canada. In our study, a team of community members, service providers, and academics partnered with three HIV community-based organizations to do this work. We used photovoice, a participatory and action-oriented photography method, to identify, document, and analyze participants' understandings at respective sites. Through collaborative analysis, we identified seven themes that may catalyze conversations about engagement within organizations: reflecting on journey; honoring relationships; accessibility and support mechanisms; advocacy, peer leadership, and social justice; diversity and difference; navigating grief and loss; and nonparticipation. Having frank and transparent discussions that are grounded in stakeholders' subjective experiences, and the sociopolitical and structural conditions of involvement, can help organizations take a more intersectional and nuanced approach to community engagement. Together, our findings can be used as a framework to support organizations in thinking more deeply and complexly about how to meaningfully, ethically, and sustainably engage communities (both individually and collectively) in HIV programming, and organizational policy change. The article concludes with questions for practice.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".