MétaCan
Menu
Back to cohort
Record W4220817426 · doi:10.33137/utjph.v9999i1.38113

Health TrueInfo: A multilingual Android app and social media approach in tackling COVID-19 vaccine misinformation and hesitancy in Bolivia, India, and Canada

2022· article· en· W4220817426 on OpenAlexaffabout
Sapolnach Prompiengchai, Neda Maki, Mahika Jain, Libertad Rojas, Jaiditya Dev, Thushanth Sriskandarajah

Bibliographic record

VenueUniversity of Toronto Journal of Public Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsMisinformationSocial mediaInternet privacyHealth communicationPublic relationsPolitical scienceMedicinePsychologyComputer science

Abstract

fetched live from OpenAlex

COVID-19 vaccine misinformation has been fueling vaccine hesitancy, which has been one of the main factors in slowing down the vaccination rate (Loomba et al., 2021). An increase in vaccine hesitancy, especially among the vulnerable communities, will exacerbate the already overwhelming economic and health burden of COVID-19. The purpose of Health TrueInfo is to use knowledge translation strategies to implement evidence-based health communications via social media in order to tackle COVID-19 vaccine misinformation and hesitancy among vulnerable populations in Bolivia, India, and Canada. The Health TrueInfo initiative is in collaboration with health experts and community members, asking them to create audiovisuals that convey powerful and culturally relevant messages to their communities. Such content combats local misinformation and encourages vaccine uptake. The audiovisual content is then uploaded to our multilingual Android app and social media platforms on Twitter, Facebook, Instagram, and LinkedIn.The concept of using social media to tackle misinformation was informed from systematic reviews, highlighting its potential by health organizations to combat prevalent misinformation as social media is widely used to share and seek health information (Chou et al., 2018; Suarez-Lledo & Alvarez-Galvez, 2021). Community engagement and searching grey literature were important methodologies to understand different local contexts of misinformation. For instance, to better comprehend how misinformation plays a role in increasing vaccine hesitancy among the Indigenous Quechua peoples in Bolivia, we collaborated with a Quechua social media influencer, who helped us create a skit inspired by the current local misinformation. Likewise, we have asked other stakeholders in healthcare like local teenagers, frontline doctors, and health experts to help create content addressing their respective communities. The knowledge translation strategies utilized here were to contextualize information, appeal to potential vaccine-hesitant groups, and use community engagement strategies like involving influencers to help us reach specific demographic groups and overcome linguistic and cultural barriers (Bella et al., 2021). One way to quantitatively estimate the impact of the project is through social media analytics. When contributors or influencers helped create audiovisuals and share with their followers, some of our content have reached over 1000 impressions and 200 views within targeted demographics. This initial success may imply how Health TrueInfo models the idea of health experts, social media influencers, and members of their own communities working together to reduce vaccine misinformation and hesitancy via creating multimodal social media contents, which in turn might help increase health and digital literacy, and battle social isolation. As health misinformation is a relatively new research field and vaccine hesitancy literature for countries like Bolivia and for the Indigenous communities in general are limited, Health TrueInfo can inspire participatory action research in countries and communities that are under-represented in the peer-reviewed literature to better understand different context-specific factors contributing to vaccine misinformation and hesitancy. References Chou, W. S., Oh, A., & Klein, W. M. P. (2018). Addressing Health-Related Misinformation on Social Media. Jama, 320(23), 2417-2418. https://doi.org/10.1001/jama.2018.16865 La Bella, E., Allen, C., & Lirussi, F. (2021). Communication vs evidence: What hinders the outreach of science during an infodemic? A narrative review. Integrative Medicine Research, 10(4), 100731. https://doi.org/10.1016/j.imr.2021.100731 Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., & Larson, H. J. (2021). Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nature Human Behaviour, 5(3), 337-348. https://doi.org/10.1038/s41562-021-01056-1 Suarez-Lledo, V., & Alvarez-Galvez, J. (2021). Prevalence of Health Misinformation on Social Media: Systematic Review. J Med Internet Res, 23(1), e17187. https://doi.org/10.2196/17187

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.287
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueUniversity of Toronto Journal of Public HealthSame topicMisinformation and Its ImpactsFrench-language works237,207