Associations Between Health Literacy and Chronic Disease Prevalence Among Employees in Chinese Petroleum Companies: A Cross-Sectional Study
Notice bibliographique
Résumé
Huifen Ma,1,* Yiming Su,2,* Shichao Zhao,3,* Ying Wang,4,* Xiaolin Wei,5 Haiyan Qu6 1School of Medical Management, Shandong First Medical University, Jinan, People’s Republic of China; 2School of Public Science and Public Administration, Shandong University, Qingdao, People’s Republic of China; 3School of Administration, Shandong Normal University, Jinan, People’s Republic of China; 4School of Management, Shandong University of Traditional Chinese Medicine, Jinan, People’s Republic of China; 5Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada; 6School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, USA*These authors contributed equally to this workCorrespondence: Shichao Zhao, School of Administration, Shandong Normal University, Jinan, People’s Republic of China, Email zhaozhao1988320@163.com Ying Wang, School of Health Management, Shandong University of Traditional Chinese Medicine, Jinan, People’s Republic of China, Email yingwang_2016@163.comBackground: Employees in the petrochemical industry are exposed to numerous occupational hazards, contributing to a higher prevalence of chronic diseases. Health literacy, which reflects an individual’s ability to access, understand, and use health information, is a critical factor in managing chronic diseases. However, its specific role in this workforce is not well understood.Objective: This study investigates the associations between health literacy and the prevalence and number of chronic diseases among employees in a Chinese petrochemical company.Methods: In March 2022, a cross-sectional survey collected 39,491 valid responses from employees of a large petrochemical company in Shandong Province, China. Health literacy was measured using the National Health Literacy Monitoring Questionnaire, while chronic disease prevalence and number were self-reported. Logistic and linear regression were used to examine associations between health literacy and chronic disease prevalence and count, respectively.Results: Among respondents, 72.1% reported at least one chronic disease, and 53.9% were classified as having adequate health literacy. The domain of Health-Related Skills had the lowest qualification rate (46.4%), and the dimension of Chronic Disease Prevention and Control was the lowest-scoring dimension (33.0%). Overall health literacy was not significantly associated with chronic disease prevalence but was negatively associated with the number of chronic diseases (B = − 0.05, 95% CI: − 0.08 - − 0.02, p < 0.001). Notably, higher literacy in Chronic Disease Prevention and Control was significantly associated with both reduced prevalence (OR = 0.95, 95% CI: 0.90– 1.00, p = 0.034) and fewer chronic diseases (B = − 0.01, 95% CI: − 0.02– 0.00, p = 0.004).Conclusion: While overall health literacy was not significantly associated with chronic disease prevalence, it was negatively associated with the number of chronic diseases. Moreover, health literacy in Chronic Disease Prevention and Control showed significant associations with both lower prevalence and fewer chronic diseases.Keywords: health literacy, chronic disease, petrochemical companies, health management, health promotion
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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 | 0,001 |
| 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 | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».