Conference equity in global health: a systematic review of factors impacting LMIC representation at global health conferences
Bibliographic record
Abstract
INTRODUCTION: Global health conferences are important platforms for knowledge exchange, decision-making and personal and professional growth for attendees. Neocolonial patterns in global health at large and recent opinion reports indicate that stakeholders from low- and middle-income countries (LMICs) may be under-represented at such conferences. This study aims to describe the factors that impact LMIC representation at global health conferences. METHODS: A systematic review of articles reporting factors determining global health conference attendance was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles presenting conference demographics and data on the barriers and/or facilitators to attendance were included. Articles were screened at title and abstract level by four independent reviewers. Eligible articles were read in full text, analysed and evaluated with a risk of bias assessment. RESULTS: Among 8765 articles screened, 46 articles met inclusion criteria. Thematic analysis yielded two themes: 'barriers to conference attendance' and 'facilitators to conference attendance'. In total, 112 conferences with 254 601 attendees were described, of which 4% of the conferences were hosted in low-income countries. Of the 98 302 conference attendees, for whom affiliation was disclosed, 38 167 (39%) were from LMICs. CONCLUSION: 'Conference inequity' is common in global health, with LMIC attendees under-represented at global health conferences. LMIC attendance is limited by systemic barriers including high travel costs, visa restrictions and lower acceptance rates for research presentations. This may be mitigated by relocating conferences to visa-friendly countries, providing travel scholarships and developing mentorship programmes to enable LMIC researchers to participate in global conferences.
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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 | 0.040 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".