Greater Improvements in Vaccination Outcomes Among Black Young Adults With Vaccine-Resistant Attitudes in the United States South Following a Digital Health Intervention: Latent Profile Analysis of a Randomized Control Trial
Notice bibliographique
Résumé
Background: Negative attitudes toward vaccines and suboptimal vaccination rates among African American and Black (Black) Americans have been well documented, due to a history of medical racism and human rights violations in the United States. However, digital health interventions (DHI) have been shown to address racial disparities in several health outcomes, such as cardiovascular disease, HIV, and maternal health. The Tough Talks COVID (TT-C) study was a randomized controlled trial of a DHI designed to empower Black young adults in the United States South to make informed, autonomous decisions about COVID-19 vaccine uptake by addressing structural barriers and misinformation about vaccines. Objective: Our objective was to identify subgroups of Black young adults with various vaccine attitudes at baseline and determine the subgroups for which the TT-C DHI was most impactful. Methods: Black young adults aged 18-29 years in Alabama, Georgia, and North Carolina who were unvaccinated or insufficiently vaccinated against COVID-19 completed three online surveys over three months (N=360). Latent profile analysis was used to identify subgroups based on general vaccine attitudes at baseline, including hesitancy, confidence, knowledge, conspiracy beliefs, and mistrust. Logistic regression was used to examine the associations between latent profiles and vaccine uptake, and linear regression was used to examine changes in vaccine attitudes at three months post-randomization. Modification of the TT-C DHI's effects was assessed by latent profiles. Results: Three latent profiles emerged: vaccine-receptive (n=124), vaccine-neutral (n=155), and vaccine-resistant (n=81). Political affiliation, income, social support, and recent flu vaccination differed significantly between the three subgroups (P<.05). Vaccine uptake was not significantly different by subgroup, and the TTC-DHI did not have differing effects on uptake across subgroups. However, the DHI had the strongest effect-with statistically significant measures of association (P<.05) and interaction P values (P<.10)-among the baseline vaccine-resistant and vaccine-neutral subgroups compared to the vaccine-receptive subgroups at three months in improving vaccine hesitancy, confidence, and conspiracy beliefs at three months: vaccine-resistant difference: -0.40 (-0.76 to -0.37), 0.39 (0.02 to 0.75), and -0.47 (-0.86 to -0.09); vaccine neutral difference: -0.36 (-0.52 to -0.19), 0.35 (0.18 to 0.51), and -0.24 (-0.44 to -0.03). The DHI had no effects on these outcomes among the vaccine-receptive subgroup. Conclusions: Our findings revealed subgroups of Black young adults in the United States South with different vaccination attitudes, for which the TT-C intervention had differing effects. Black young adults who are vaccine-resistant or vaccine-neutral may experience larger gains from a digital vaccine intervention. Future work aimed at improving vaccination outcomes could target these populations to maximize resource efficiency and drive the greatest improvements in vaccine outcomes.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».