Use of #SaludTues Tweetchats for the Dissemination of Culturally Relevant Information on Latino Health Equity: Exploratory Case Study
Bibliographic record
Abstract
BACKGROUND: Latinx people comprise 18% of the US adult population and a large share of youth and continue to experience inequities that perpetuate health disparities. To engage Latinx people in advocacy for health equity based on this population's heavy share of smartphone, social media, and Twitter users, Salud America! launched the #SaludTues Tweetchat series. In this paper, we explore the use of #SaludTues to promote advocacy for Latinx health equity. OBJECTIVE: This study aims to understand how #SaludTues Tweetchats are used to promote dissemination of culturally relevant information on social determinants of health, to determine whether tweetchats serve to drive web traffic to the Salud America! website, and to understand who participates in #SaludTues Tweetchats and what we can learn about the participants. We also aim to share our own experiences and present a step-by-step guide of how tweetchats are planned, developed, promoted, and executed. METHODS: We explored tweetchat data collected between 2014 and 2018 using Symplur and Google Analytics to identify groups of stakeholders and web traffic. Network analysis and mapping tools were also used to derive insights from this series of chats. RESULTS: We conducted 187 chats with 24,609 reported users, 177,466 tweets, and more than 1.87 billion impressions using the hashtag #SaludTues during this span, demonstrating effective dissemination of and exposure to culturally relevant information. Traffic to the Salud America! website was higher on Tuesdays than any other day of the week, suggesting that #SaludTues Tweetchats acted effectively as a website traffic-driving tool. Most participants came from advocacy organizations (165/1000, 16.5%) and other health care-related organizations (162/1000, 16.2%), whereas others were unknown users (147/1000, 14.7%) and individual users outside of the health care sector (117/1000, 11.7%). The majority of participants were located in Texas, California, New York, and Florida, all states with high Latinx populations. CONCLUSIONS: Carefully planned, culturally relevant tweetchats such as #SaludTues can be a powerful tool for public health practitioners and advocates to engage audiences on Twitter around health issues, advocacy, and policy solutions for Latino health equity. Further information is needed to determine the effect that #SaludTues Tweetchats have on self- and collective efficacy for advocacy in the area of Latino health equity.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".