An Exploratory Study of Taiwanese Consumers' Experiences of Using Health-Related Websites
Bibliographic record
Abstract
It is manifest that the rapid growth of Internet use and improvement of information technology have changed our lifestyles. In recent years, Internet use in Taiwan has increased dramatically, from 3 million users in 1998 to approximately 8.6 million by the end of 2002. The statistics imply that not only health care professionals but also laypersons rely on the Internet for health information. The purpose of this study was to explore Taiwan consumers' preferences and information needs, and the problems they encountered when getting information from medical websites. Using simple random sampling and systematic random sampling, a survey was conducted in Taipei from August 26, 2002 to October 30, 2002. Using simple random sampling and systematic random sampling, 28 boroughs (Li) were selected; the total sample number was 1043. Over one-quarter (26.8 %) of the respondents reported having never accessed the Internet, while 763 (73.2%) reported having accessed the Internet. Of the Internet users, only 396 (51.9%) had accessed health-related websites, and 367 (48.1%) reported having never accessed health-related websites. The most popular topics were disease information (46.5%), followed by diet consultation (34.8%), medical news (28.5%), and cosmetology (28.5%). The results of the survey show that a large percentage of people in Taiwan have never made good use of health information available on the websites. The reasons for not using the websites included a lack of time or Internet access skills, no motivation, dissatisfaction with the information, unreliable information be provided, and inability to locate the information needed. The author recommends to enhance health information access skills, understand the needs and preferences of consumers, promote the quality of medical websites, and improve the functions of medical websites.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".