Breast cancer information communicated on a public online platform: an analysis of ‘Yahoo! Answer Japan’
Bibliographic record
Abstract
INTRODUCTION Japan is a developed country with high use of Internet and online platforms for health information. 'Yahoo! Answer Japan' is the most commonly used question-and-answer service in Japan. AIM To explore the information users seek regarding breast cancer from the 'Yahoo! Answer Japan' web portal. METHODS The 'Yahoo! Answer Japan' portal was searched for the key word 'breast cancer' and all questions searched for the period of 1 January to 31 December 2014 were obtained. The selected questions related to human breast cancer and were not advertisements or promotional material. The questions were categorized using a coding schema. High and low access of the questions were defined by the number of view-counts. RESULTS Among the 2392 selected questions, six major categories were identified; (1) suspected breast cancer, (2) breast cancer screening, (3) treatment of breast cancer, (4) life with breast cancer, (5) prevention of breast cancer and (6) others. The highest number of questions were treatment related (28.8%) followed by suspected breast cancer-related questions (23.4%) and screening-related questions (20%). Statistical analysis revealed that the treatment-related questions were more likely to be highly accessed. CONCLUSION Content analysis of Internet question-answer communities is important, as questions posted on these sites would serve as a rich source of direct reflection regarding the health-related information needs of the general 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".