Cross‐cultural quality comparison of online health information for elderly care on yahoo! answers
Bibliographic record
Abstract
ABSTRACT Given the increase in global aging population, popularity of social Q&A sites and the level of geriatric health concerns from family caregivers, it raises the uncertainty about the quality of health information on community Q&A sites for family caregivers of elderly. The purpose of this study is to evaluate the quality of geriatric health information on social Questions and Answers (Q&A) sites: Yahoo! Answers from registered nurses’ perspective. A total of 60 question‐and‐answer sets are retrieved from regional Yahoo! Answers sites, including Australia, Canada, UK & Ireland, US, Hong Kong, Mainland China and Taiwan. A total of 126 English answers and 112 Chinese answers were examined by registered nurses and library professionals. Results show that the overall information quality provided in the Chinese group is relatively poorer than those of English especially in information quality dimensions such as Verifiability, Commercialisation and Completeness. Questioners form both the English and Chinese groups might possibly miss the best pieces of information and advices regarding the health concerns of their elderly family members since about 40% of the best answers they selected did not match health professionals’ picks.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".