Creating Socially Accountable Health Conferences: Guidance from Around the World
Bibliographic record
Abstract
BACKGROUND: Very little attention has been given to the social accountability of conferences, either in action or in scholarship, in particular, of scientific conferences. Concerns that have been raised include: (1) Local communities and regions suffer from ecological pressure caused by conferences, (2) There is limited value to the local community, (3) International conferences take place at locations irrelevant to the topics discussed; hence there is no connection with locals, and (4) It has been the observation of the authors that <10% of participants may come from the region where the conference is organized, which makes it challenging to make a "positive societal impact" locally. We conducted a natural experiment investigating the interactions between academia, conference organizers, and community leaders. METHODS: We utilized a case study approach to report on the outcomes of two 2022 annual international conferences that seek to improve community health. We used a mixed-methods approach of surveys and interviews. Thematic analysis was conducted to identify the key themes. RESULTS: We obtained 358 responses from all six World Health Organization regions. Results from both conferences were split into two categories: the why and the how. A strong consensus among participants is that bi-directional learning between conference organizers and local communities leads to shared understanding and mutual goals. The data emphasize that including communities in academic conferences helps us progress forward from intentions toward demonstrating accountability and reporting impact. DISCUSSION: A diversity of perspectives is needed to advance socially accountable health system transformation. Five best practices from conference participants are laid out as a framework to assist in the change: (1) Build trust, (2) provide funding for community member participation, (3) appreciation of local community knowledge, (4) involve the local community in the planning stages, and (5) make the local community part of the conference and learning.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".