Patient Education Level and Utilization of Internet Resources by Patients in Orthopedic Hip and Knee Consultations
Bibliographic record
Abstract
Introduction: Internet has become an increasingly popular source of reference for patients to learn about their medical problems. It is easily accessible, and a large number of uncensored information is available online written from various sources and perspectives. However, the role of internet and its impact on patient’s care and understanding of the disease remains unclear. The purpose of this study is to evaluate the role and effect of internet use for patients seeking consultation for hip and knee arthritis. More specifically, the relationship between patient’s education level, internet use, motives for doing background readings, perception of internet information, and reactions to the available information will be studied. Method: Patients seeking orthopaedic consultation for knee or hip arthritis at the Toronto Western Hospital were identified and invited to fill out a questionnaire on their first visit. The questionnaire was designed to assess the patients’ pre-consultation reading habits, their use of internet, and their reaction to what they have read on the internet. The questionnaire also included questions about the respondent’s background.Results: In comparing patients holding college/university degree (CU) with patients having no college/university education (NoCU), the CU group were associated with increased internet use (CU vs. NoCU: 71.0% vs. 48.3%; p 0.01) and background reading (CU vs. NoCU: 82.2% vs. 17.8%; p 0.001) prior to consultation; fewer incidence of anxiety following internet use (CU vs. NoCU: 29.9% vs. 53.6%; p 0.05); and higher rates of decisions influenced by internet use (CU vs. NoCU: 20.8% vs. 3.6%, p 0.05). Internet users demonstrated a higher confidence in gathering and understanding medical information (Internet users vs. non-internet users: 6.59 ± 2.05 vs. 5.03 ± 2.78; p 0.001) and rated the accuracy of information on internet at 7.18 ± 2.01 (max = 10). Conclusion: Internet use can influence patient’s treatment decision, anxiety level, and understanding of their disease. Caregivers must recognize the growing trend of internet use and should counsel and educate their patients appropriately based on what they have read to help them accurately appreciate the nature of their disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".