Food Delivery Drivers’ Health Literacy Regarding COVID-19 Prevention and Protective Behaviors During the COVID-19 Pandemic: Cross-sectional Survey in Southern Thailand
Notice bibliographique
Résumé
BACKGROUND: In 2019, COVID-19 spread worldwide, causing a pandemic that has posed unprecedented challenges and pressure for health systems and economies. Food delivery services have become an important medium for consumer food purchases to limit human-to-human contact. Thus, delivery drivers are at high risk of exposure to COVID-19 infection at work. To the best of our knowledge, no studies have analyzed the dimensions of health literacy (HL) regarding COVID-19 prevention in this population. OBJECTIVE: This study aims to explore the HL status toward COVID-19 prevention and its associated factors among food delivery drivers in southern Thailand. METHODS: Following a cross-sectional survey from July to August 2021, Thai food delivery drivers in the upper-south and lower-south regions of southern Thailand were recruited to participate during the compulsory COVID-19 lockdown. An online structured questionnaire was administered verbally and recorded by the interviewer. Univariate and multivariate linear regressions were used to explore independently associated factors. RESULTS: Of 401 drivers, 291 (72.6%) were men. The median age was 31 years (range 19-64 years). The median number of months working as a driver was 12 months, and the median number of working hours was 9 hours per day. The median number of daily food orders was 20, while the median daily income was Thai baht (THB) 600 (US $15.90). Social media (Facebook and Line) was a common source of health information. The most common information required was about the COVID-19 vaccine, medications, and treatment. Most drivers (285/401, 71.1%) had excellent HL levels regarding COVID-19 prevention. Only the practical application of information was statistically correlated with behavior (r=0.38, P<.001). Drivers in the lower south of Thailand were more likely to have excellent HL than other drivers (β=7.03, P<.001). Those who frequently accessed information through YouTube (β=-2.17, P=.01) and relatives (β=-4.19, P<.001) were less likely to have excellent HL levels. CONCLUSIONS: Understanding HL among food delivery drivers would be useful for planning effective interventions that target this population. Conventional health education through social media alone may not be effective at educating people about COVID-19 prevention. Information literacy skills could determine individuals' HL and drivers' behaviors.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».