Associations Between Health Literacy and Chronic Disease Prevalence Among Employees in Chinese Petroleum Companies: A Cross-Sectional Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".