Cross‐cultural quality comparison of online health information for elderly care on yahoo! answers
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
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it