Quality evaluation of geriatric health information on Yahoo! Answers : a cross-cultural comparative study
Bibliographic record
Abstract
Given the increases on global ageing 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 social Q&A sites for family caregivers of elderly. The purposes of this study are to evaluate the quality of geriatric health information on social Questions and Answers (Q&A) sites: Yahoo! Answers from registered nurses’ perspective, to identify the structural patterns of questions and answers vary in quality and to discover the cultural aspects in relation to the findings. A total of 60 question-answers set is retrieved from regional Yahoo! Answers sites, including Australia, Canada, UK & Ireland, US, Hong Kong, Mainland China and Taiwan. 126 English answers and 112 Chinese answers are examined. \n \nThrough a mixed method approach, results show that the overall information quality provided in Chinese group is relatively poorer than those of English. About 40% of questioners form both groups are not capable of judging the best answer among choices. In terms of structural patterns, questioners from both language groups are less capable of asking questions with clear focuses. 4 structural patterns, including Chinese and English answers with good and poor quality, are identified. Furthermore, cultural differences are found to have a significant impact on the level of information quality in social Q&A site. Finally, recommendations to corresponding social sectors are made for improving the current information quality of social Q&A sites in future.
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".