Influencing Factors of Consumer Health Information Seeking Behavior Via Social Media
Bibliographic record
Abstract
Objective : We aim to analyze the consumer health information seeking behavior to figure out its characteristics and influencing factors, and make further efforts to provide targeted recommendations for media managers to promote health communication via social media. Design/methodology/approach : Our custom model was derived from literature review, empirical research was tested by the use of questionnaire investigation, and finally the collected data was analyzed by SmartPLS, a tool of structural equation model. Findings : Gratification of health information and its platform had a positive effect on attitudes toward health information seeking behavior. Health information literacy was proved to have a significant influence on attitudes toward health information seeking behavior, subject norms and perceived behavioral control, respectively. Attitudes toward the health information seeking behavior and subject norms were proved to positively associate with health information seeking behavior intention. In addition, some demographic factors were found to associate with health information seeking behavior via social media such as age, gender, and profession. Originality/value : We constructed the seeking behavior model of health information from the perspective of sociology and psychology, empirically studied health information seeking behavior and its influencing factors via social media, and has laid a favorable foundation for the relevant departments about further health communication research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".