The Mechanism of Online Health Information Seeking Switching to Online Medical Consultation: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Internet health care plays a crucial role in addressing the challenge of distributing high-quality medical resources and promoting the optimal allocation of these resources and health equity in China. Online medical consultation (OMC) plays a more significant role than online health information seeking (OHIS). Currently, the proportion of Chinese patients using OMC is low. Therefore, it is essential to enhance patient engagement with OMC and fully leverage the role of internet health care in optimizing the allocation of medical resources. OBJECTIVE: This study aims to explore the correlation mechanisms of online medical community users' switching behaviors from OHIS to OMC. METHODS: This study is based on the knowledge-attitude-practice theory, which combines the social support theory and the health belief model to construct a research model of users' willingness to transition from OHIS to OMC. The study adopts a questionnaire survey and structural equation modeling method to conduct an empirical study. RESULTS: Gaining knowledge about information support has a significant positive impact on perceived susceptibility (β=.339, P<.001), perceived severity (β=.348, P<.001), and perceived benefits (β=.361, P<.001), while having a significant negative impact on perceived barriers (β=-.285, P<.001). Gaining knowledge about emotional support positively affects perceived susceptibility (β=.220, P<.001) and perceived benefits (β=.149, P<.01) but does not significantly influence perceived severity (β=-.006, P>.05) or perceived barriers (β=.099, P>.05). Perceived susceptibility (β=.123, P<.05), perceived severity (β=.174, P<.001), and perceived benefits (β=.273, P<.001) positively influence patients' transition to online consultation behavior, whereas perceived barriers (β=-.112, P<.05) negatively impact this switch. In addition, we found that gaining knowledge about information support not only directly affects patients' behavior in switching to online consultations but also impacts patients' OMCs through perceived susceptibility (14.23%), perceived severity (13.17%), and perceived benefits (25.28%). In contrast, gaining knowledge about emotional support does not directly influence patient behavior transfer; it operates only through perceived susceptibility (46.95%) and perceived benefit (52.90%). CONCLUSIONS: This study integrated the knowledge-attitude-practice framework, social support theory, and health belief model to uncover the internal logic of patients' behavioral transfers within online health communities. It confirmed the mediating role of the cognitive-emotional dual-drive pathway and health beliefs. The findings provide a scientific basis for the functional design of online health care platforms and for precise health knowledge dissemination strategies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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) | 0.003 | 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".