Hashtag Global Surgery: The Role of Social Media in Advancing the Field of Global Surgery
Bibliographic record
Abstract
INTRODUCTION: Surgery is increasingly recognized as an indispensable part of healthcare, but lack of awareness about its cost-effectiveness and cross-cutting impact remain. Social media has become an important resource for healthcare professionals in a variety of settings due to its instant global reach in a non-discriminatory and low-threshold manner. In 2010, #globalsurgery was first used on Twitter to spread awareness, foster international collaborations, and raise voices of advocates around the world. Here, we examine the role of social media in the field of global surgery. METHODS: The use of #globalsurgery on Twitter was analyzed through Tweetreach from July 31 to December 31, 2018. Additional analysis of hashtags in Spanish, Japanese, Malay, and Portuguese was done to determine the number of tweets, retweets, impressions, and users using #globalsurgery or translated hashtags. Sentiment analysis was performed to determine the affective state of tweets. RESULTS: A total of 4,519 tweets and 15,861 retweets were posted by 4,449 different contributors. Tweets totalled 58,733,406 potential direct impressions and 46,560,293 potential amplified impressions, with potential reach of 11,272,014. English was the major language (99.47%), followed by Spanish (0.49%) and Japanese (0.04%). Portuguese and Malay hashtags were not used during the study period. CONCLUSION: #globalsurgery provides an innovative way to overcome barriers and strengthen collaboration among advocates, and more effectively raise awareness about global surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".