How, When and Why People Seek Health Information Online: Qualitative Study in Hong Kong
Bibliographic record
Abstract
BACKGROUND: The Internet has become an established source for health information. The number of individuals using the Internet to search for health information, ranging from healthy lifestyle advice to treatment and diseases, continues to grow. Scholars have emphasized the need to give greater voice and influence to health consumers. Hong Kong, being one of the most technologically advanced and connected cities in the world, has one of the highest Internet penetration rates in the world. Given the dearth of research in an Asian context, Hong Kong is an excellent platform to study individuals' perceptions (eg, benefits and limitations on seeking health information online and how the information is used) on health information seeking. OBJECTIVE: The aim of this paper was to study individuals' perceptions on health information seeking and to document their Internet information-seeking behaviors. METHODS: Five focus groups (n=49) were conducted from November 2015 to January 2016 with individuals across different age groups (18 years or above). Focus group contents were audiotaped, transcribed, and analyzed using thematic analysis techniques. RESULTS: Older (55+ years) and less educated respondents were less likely to use the Internet to search for health information. Among individuals who obtained health information via the Internet, regardless of the severity of the health issue, the Internet was always the first source for information. Limited doctor consultation time and barriers to accessing professional health services were the main reasons for using the Internet. Convenience and coverage were regarded as the main advantages, whereas credibility and trustworthiness of health information were noted as limitations. The use of Web-based health information varied among individuals; hence, the implications on the doctor-patient relationship were mixed. CONCLUSIONS: The prevalent and increasing use of the Internet for health information seeking suggests the need for health care professionals to understand how it can be optimally utilized to improve health outcomes. Strategies for communicating and disseminating credible health information in a form that users can understand and use are essential. Due to the rapid technological and related behavioral changes, online health information seeking and its effects need to be closely monitored.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".