Are Physicians Ready for Patients With Internet-Based Health Information?
Bibliographic record
Abstract
BACKGROUND: An increasing number of patients bring Internet-based health information to medical consultations. However, little is known about how physicians experience, manage, and view these patients. OBJECTIVE: This study aimed to advance the understanding of the effects of incorporating Internet-based health information into routine medical consultations from physicians' perspectives, using a qualitative approach. METHODS: Six focus groups were conducted with 48 family physicians practising in Toronto. The data were analyzed using qualitative methods of content analysis and constant comparison, derived from grounded theory approach. RESULTS: Three overarching themes were identified: (1) perceived reactions of patients, (2) physician burden, and (3) physician interpretation and contextualization of information. Physicians in our study generally perceived Internet-based health information as problematic when introduced by patients during medical consultations. They believed that Internet information often generated patient misinformation, leading to confusion, distress, or an inclination towards detrimental self-diagnosis and/or self-treatment. Physicians felt these influences added a new interpretive role to their clinical responsibilities. Although most of the physicians felt obliged to carry out this new responsibility, the additional role was often unwelcome. Despite identifying various reactions of patients to Internet-based health information, physicians in our study were unprepared to handle these patients. CONCLUSION: Effective initiatives at the level of the health care system are needed. The potential of Internet-based health information to lead to better physician-patient communication and patient outcomes could be facilitated by promoting physician acknowledgment of increasing use of the Internet among patients and by developing patient management guidelines and incentives for physicians.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".