Addressing Behavioral Barriers to COVID-19 Testing With Health Literacy–Sensitive eHealth Interventions: Results From 2 National Surveys and 2 Randomized Experiments
Notice bibliographique
Résumé
BACKGROUND: Polymerase chain reaction (PCR) testing for COVID-19 was crucial in Australia's prevention strategy in the first 2 years of the pandemic, including required testing for symptoms, contact with cases, travel, and certain professions. However, several months into the pandemic, half of Australians were still not getting tested for respiratory symptoms, and little was known about the drivers of and barriers to COVID-19 PCR testing as a novel behavior at that time. OBJECTIVE: We aimed to identify and address COVID-19 testing barriers, and test the effectiveness of multiple eHealth interventions on knowledge for people with varying health literacy levels. METHODS: The intervention was developed in 4 phases. Phase 1 was a national survey conducted in June 2020 (n=1369), in which testing barriers were coded using the capability-opportunity-motivation-behavior framework. Phase 2 was a national survey conducted in November 2020 (n=2034) to estimate the prevalence of testing barriers and health literacy disparities. Phase 3 was a randomized experiment testing health literacy-sensitive written information for a wide range of barriers between February and March 2021 (n=1314), in which participants chose their top 3 barriers to testing to view a tailored intervention. Phase 4 was a randomized experiment testing 2 audio-visual interventions addressing common testing barriers for people with lower health literacy in November 2021, targeting young adults as a key group endorsing misinformation (n=1527). RESULTS: In phase 1, barriers were identified in all 3 categories: capability (eg, understanding which symptoms to test for), opportunity (eg, not being able to access a PCR test), and motivation (eg, not believing the symptoms are those of COVID-19). Phase 2 identified knowledge gaps for people with lower versus higher health literacy. Phase 3 found no differences between the intervention (health literacy-sensitive text for top 3 barriers) and control groups. Phase 4 showed that a fact-based animation or a TikTok-style video presenting the same facts in a humorous style increased knowledge about COVID-19 testing compared with government information. However, no differences were found for COVID-19 testing intentions. CONCLUSIONS: This study identified a wide range of barriers to a novel testing behavior, PCR testing for COVID-19. These barriers were prevalent even in a health system where COVID-19 testing was free and widely available. We showed that key capability barriers, such as knowledge gaps, can be improved with simple videos targeting people with lower health literacy. Additional behavior change strategies are required to address motivational issues to support testing uptake. Future research will explore health literacy strategies in the current context of self-administered rapid antigen tests. The findings may inform planning for future COVID-19 variant outbreaks and new public health emergencies where novel testing behaviors are required. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621000876897, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=382318 ; Australian New Zealand Clinical Trials Registry ACTRN12620001355965, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380916&isReview=true.
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 | 0,048 | 0,064 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,008 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».