Preventive health information on the Internet. Qualitative study of consumers' perspectives.
Bibliographic record
Abstract
OBJECTIVE: To explore how best to make high-quality preventive health information available to consumers on the Internet. DESIGN: Focus groups. SETTING: Three urban workplaces and one local hospital with patients from a rural family medical practice. PARTICIPANTS: Twenty-two men and 17 women patients. METHOD: Qualitative survey of four focus groups, analysis of transcripts and researchers' notes. MAIN FINDINGS: Five themes characterized participants' perceptions of a consumer website of evidence-based preventive guidelines: content expectations, website design, trustworthiness of content, marketing, and the implications of consumer health information on the Internet. CONCLUSION: Consumers want preventive health information both for taking care of themselves and for participating in a more informed way in their health care when they see a physician. Findings of this study reveal some ways in which consumers' use of Internet health information can affect physicians' and other health professionals' work.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".