Is global health truly global? A hashtag analysis of #GlobalHealth disparities on X
Bibliographic record
Abstract
Background X (Formerly known as Twitter) healthcare hashtags are a popular healthcare informatics and educational tool among medical professionals. #Globalhealth is one such widely used hashtag with extensive engagement. This study analyses #GlobalHealth to understand its pattern, global digital distribution, and other parameters during the COVID-19 pandemic on X. Methods Data was collected by utilizing posts using #GlobalHealth on X from 1st December 2019 to 1st November 2022. The analysis was performed using Symplur Signals to assess several parameters, such as the cumulative number of posts, impressions, category of users, co-occurring hashtags, and geolocation. The Symplur Rank system was used to assess the impact of influencers using the hashtag. Results A total of 843,762 posts were shared by 150,408 X users, with 4,639,144,304 impressions. Most posts (73.8%) were made by unclassified accounts, followed by doctors (4.2%), followed by other health workers. The #COVID19 was the most common co-occurring hashtag (43%). The top locations and the most influential X users came from the United States, the United Kingdom, and Canada. Among the top 25 most influential handles, a maximum (N = 09) were based in the United States—most profiles (N = 10) were categorized as international organizations followed by journals (N = 03). Conclusion The study gives a glimpse into the discrepancies in global distribution and stakeholders of #GlobalHealth. Most posts originated from the global north, which hints at how the trend to #GlobalHealth is not perhaps as global as it is thought to be, and it also reflects upon the real-world scenarios in the context of Global Health Equity. Thus, deeper and wider studies on this digital discrepancy may add more to the existing discourse on the topic.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".