What Are Patients Seeking When They Turn to the Internet? Qualitative Content Analysis of Questions Asked by Visitors to an Orthopaedics Web Site
Bibliographic record
Abstract
BACKGROUND: More people than ever are turning to the Internet for health-related information, and recent studies indicate that the information patients find online directly affects the decisions they make about their health care. Little is known about the information needs or actual search behavior of people who use the Internet for health information. OBJECTIVE: This study analyzes what people search for when they use a health-education Web site offering information about arthritis, orthopaedics, and sports-medicine topics. Additionally, it determines who is performing these searches: is it patients, friends or relatives of patients, or neither? Finally, it examines the similarities and differences among questions submitted by Web site visitors from different countries. METHODS: Content analysis was performed on 793 free-text search queries submitted to a patient-education Web site owned and operated by the Department of Orthopaedics and Sports Medicine at the University of Washington Medical Center. The 793-query data set was coded into 3 schemes: (1) the purpose of the query, (2) the topic of the query, and (3) the relationship between the asker of the query and the patient. We determined the country from which each query was submitted by analyzing the Internet Protocol addresses associated with the queries. RESULTS: The 5 most frequent reasons visitors searched the Web site were to seek: (1) information about a condition, (2) information about treatment, (3) information about symptoms, (4) advice about symptoms, and (5) advice about treatment. We were able to determine the relationship between the person submitting the query and the patient in question for 178 queries. Of these, the asker was the patient in 140 cases, and the asker was a friend or relative of the patient in 38 cases. The queries were submitted from 34 nations, with most coming from the United States, Australia, the United Kingdom, and Canada. When comparing questions submitted from the United States versus those from all other countries, the 3 most frequent types of questions were the same for both groups (and were the top 3 question types listed above). CONCLUSIONS: These results provide the University of Washington Department of Orthopaedics and Sports Medicine, as well as other organizations that provide health-information Web sites, with data about what people around the world are seeking when they turn to the Internet for health information. If Web site managers can adapt their health-information Web sites in response to these findings, patients may be able to find and use Internet-based health information more successfully, enabling them to participate more actively in their health care.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".