Frequent Internet Users May Prefer More Health Care Information and Participation in Decision-Making
Bibliographic record
Abstract
Objective – To determine whether there is a significant relationship between patients’ frequency of Internet use and their health care information and decision-making preferences. Design – Cross-sectional questionnaire survey. Settings – Undergraduate classes at a large state university and senior-oriented computer classes at public libraries and senior centers. Subjects – 438 respondents, including 226 undergraduates (mean age 20) and 212 community-dwelling older adults (mean age 72). Methods – Respondents were administered the Health Information Wants Questionnaire (HIWQ), a 21-item instrument designed to measure preferences for 7 types of health information and decision-making, in group settings. Main Results – The younger age group spent significantly more time online compared to the older age group. Frequent Internet users in both populations expressed an overall preference for more information regarding diagnosis, but less information for psychosocial and health care provider concerns. Internet use was positively correlated to the overall preference rating, leading the researchers to suggest that, as a whole, regular Internet users prefer more information and independence in decision-making. Conclusions – The study concludes that Internet use frequency is associated with an overall preference for obtaining health information and participating in decision making. Internet use as related to different types of preferences is inconsistent. Age was not found to be associated with the overall preference rating, and time spent online is proposed to be a stronger indicator of respondents’ health information preferences. The authors suggest that future studies utilizing the HIWQ take a longitudinal approach in order to better track how patient preferences for information may evolve over time.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".