Why do Chinese Youth Seek Cancer Risk Information Online? Evidence from Four Cities
Bibliographic record
Abstract
This study, inspired by the Risk Information Seeking and Processing (RISP) model, examines the mechanisms by which perceived hazard characteristics and the informational subjective norms of Chinese youth, aged from 14 to 44 years old, become associated with their intentions to seek cancer risk information online. A sample of 684 Chinese youths was collected from four cities in Mainland China with results revealing that perceived hazard characteristics and informational subjective norms motivate their online cancer risk information seeking intentions. Specifically, perceived probability, perceived severity, and institutional trust are positively related to negative affect, however the relationship between personal control and negative affect is not significant. Institutional trust and personal control are positively related to positive affect while perceived probability and perceived severity have no significant effect on positive affect. Negative affect and informational subjective norms are positively related to perceived information insufficiency, while the relationship between positive affect and perceived information insufficiency is not significant. Negative affect, positive affect, informational subjective norms, and perceived information insufficiency are all positively related to the online cancer risk information seeking intentions of Chinese youth.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".