Why do Chinese Youth Seek Cancer Risk Information Online? Evidence from Four Cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it