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Record W4391184660 · doi:10.2196/51331

Latino Parents’ Reactions to and Engagement With a Facebook Group–Based COVID-19 Vaccine Promotion Intervention: Mixed Methods Pilot Study

2024· article· en· W4391184660 on OpenAlexvenueno aff
Anna González, Elizabeth Andrade, Lorien C. Abroms, Kaitlyn Gómez, Carla Favetto, Valeria M Gómez, Karen K Collins

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteNational Institutes of HealthGeorge Washington University
KeywordsSocial mediaMisinformationPsychologyPromotion (chess)Intervention (counseling)ModerationMedicineSocial psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Misinformation in Spanish on social media platforms has contributed to COVID-19 vaccine hesitancy among Latino parents. Brigada Digital de Salud was established to disseminate credible, science-based information about COVID-19 in Spanish on social media. OBJECTIVE: This study aims to assess participants' reactions to and engagement with Brigada Digital content that sought to increase COVID-19 vaccine uptake among US Latino parents and their children. METHODS: We conducted a 5-week intervention in a private, moderator-led Facebook (Meta Platforms, Inc) group with Spanish-speaking Latino parents of children aged ≤18 years (N=55). The intervention participants received 3 to 4 daily Brigada Digital posts and were encouraged to discuss the covered topics through comments and polls. To assess participants' exposure, reactions, and engagement, we used participants' responses to a web-based survey administered at 2 time points (baseline and after 5 weeks) and Facebook analytics to calculate the average number of participant views, reactions, and comments. Descriptive statistics were assessed for quantitative survey items, qualitative responses were thematically analyzed, and quotes were selected to illustrate the themes. RESULTS: Overall, 101 posts were published. Most participants reported visiting the group 1 to 3 times (22/55, 40%) or 4 to 6 (18/55, 33%) times per week and viewing 1 to 2 (23/55, 42%) or 3 to 4 (16/55, 29%) posts per day. Facebook analytics validated this exposure, with 36 views per participant on average. The participants reacted positively to the intervention. Most participants found the content informative and trustworthy (49/55, 89%), easy to understand, and presented in an interesting manner. The participants thought that the moderators were well informed (51/55, 93%) and helpful (50/55, 91%) and praised them for being empathic and responsive. The participants viewed the group environment as welcoming and group members as friendly (45/55, 82%) and supportive (19/55, 35%). The 3 most useful topics for participants were the safety and efficacy of adult COVID-19 vaccines (29/55, 53%), understanding child risk levels (29/55, 53%), and the science behind COVID-19 (24/55, 44%). The preferred formats were educational posts that could be read (38/55, 69%) and videos, including expert (28/55, 51%) and instructional (26/55, 47%) interviews. Regarding engagement, most participants self-reported reacting to posts 1 to 2 (16/55, 29%) or 3 to 4 (15/55, 27%) times per week and commenting on posts 1 to 2 (16/55, 29%) or <1 (20/55, 36%) time per week. This engagement level was validated by analytics, with 10.6 reactions and 3 comments per participant, on average, during the 5 weeks. Participants recommended more opportunities for engagement, such as interacting with the moderators in real time. CONCLUSIONS: With adequate intervention exposure and engagement and overall positive participant reactions, the findings highlight the promise of this digital approach for COVID-19 vaccine-related health promotion.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.291
GPT teacher head0.562
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2024
Admission routes1
Has abstractyes

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