The Attitudes of Egyptian Web-Based Health Information Seekers Toward Health Information Provided Through the Internet: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The internet has become an established source of health information for many Egyptian internet users. Understanding users' attitudes toward the benefits and limitations of web-based health information will explain the influence of this information on users' health-related behavior and decisions. OBJECTIVE: This qualitative study aims to understand the attitude of Egyptian internet users toward internet health information and to explore the impact of obtained health information on users' behavior and on their physician-patient relationship. METHODS: For this qualitative study, semistructured interviews were conducted with a total of 49 participants (41/49, 84% Egyptian internet users and 8/49, 16% physicians) who participated in focus groups or individual interviews. We used a thematic analysis approach to explain and demonstrate participants' views, thoughts, and experiences in using web-based health information. RESULTS: The internet has become an important source of health information in comparison with other health information sources and is the central theme that has emerged across the thematic analysis. The attitude toward the use of internet health was classified into three main themes: feeling toward web-based health information (with subthemes: favoring, disliking, neutral, or having ambivalence feelings), motivators to seek internet health information, and behavioral changes using internet health information (subthemes: confidence, satisfaction, and improved knowledge). Themes that emerged from physicians' interviews included the accessibility of the internet health information, good communication, and coordination of care between patients and their physicians, and the active engagement of patients with their management plan. CONCLUSIONS: The internet has become an essential source of health information for Egyptian adults. Internet health information can improve the patient-physician relationship, especially when users discuss the obtained health information with their physician. Internet health information provided seekers with social support and self-confidence when making health decisions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".