Online Health Information–Seeking Behaviors Among the Chongqing Population: Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
Background: With the rapid development of the internet and its widespread use, online health information-seeking (OHIS) has become a popular and important research topic. Various benefits of OHIS are well recognized. However, OHIS seems to be a mixed blessing. Research on OHIS has been reported in Western countries and in high-income regions in eastern China. Studies on the population in the western region of China, such as Chongqing, are still limited. Objective: The aim of the study was to identify the prevalence, common topics, and common methods of health information-seeking and the factors influencing these behaviors among the Chongqing population. Methods: This cross-sectional questionnaire study was conducted from September to October 2021. A web-based questionnaire was sent to users aged 15 years and older in Chongqing using a Chinese web-based survey hosting site (N=14,466). Data on demographics, web-based health information resources, and health topics were collected. Factors that may influence health literacy were assessed using the chi-square test and multivariate logistic regression models. Results: A total of 67.1% (9704/14,466) of the participants displayed OHIS behaviors. Participants who were younger, had a higher educational level, and worked as medical staff or teachers were more likely to engage in OHIS, while those living in rural areas, ethnic minorities, and farmers were less likely to seek health information on the web (P<.01). Among the Chongqing population, the most common topic searched on the internet was health behavior and literacy (87.4%, 8483/9704), and the most popular method of seeking health information on the web was through WeChat (77.0%, 7468/9704). Conclusions: OHIS is prevalent in Chongqing. Further research could be performed based on the influencing factors identified herein and high-priority, effective ways of improving the OHIS behaviors of the Chongqing population.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".