Patient-Physician Communication in the Era of Mobile Phones and Social Media Apps: Cross-Sectional Observational Study on Lebanese Physicians’ Perceptions and Attitudes
Bibliographic record
Abstract
BACKGROUND: The increased prevalence of virtual communication technology, particularly social media, has shifted the physician-patient relationship away from the well-established face-to-face interaction. The views and habits of physicians in Lebanon toward the use of online apps and social media as forms of patient communication have not been previously described. OBJECTIVE: The aim of this study is to describe the views of Lebanese physicians toward the use of social media and other online apps as means of patient communication. METHODS: This was a cross-sectional observational study using an online survey that addressed physicians' perceptions on the use of virtual communication in their clinical practice. The study took place between April and June 2016, and was directed toward physicians at the American University of Beirut Medical Center. RESULTS: A total of 834 doctors received the online survey, with 238 physicians completing the survey. Most of the participants were from medical specialties. Most responders were attending physicians. Less than half of the respondents believed that Web-based apps and social media could be a useful tool for communicating with patients. Email was the most common form of professional online app, followed by WhatsApp (an instant messaging service). The majority of participants felt that this mode of communication can result in medicolegal issues and that it was a breach of privacy. Participants strictly against the use of virtual forms of communication made up 47.5% (113/238) of the study sample. CONCLUSIONS: The majority of physicians at the American University of Beirut Medical Center are reluctant to use virtual communication technology as a form of patient communication. Appropriate policy making and strategies can allow both physicians and patients to communicate virtually in a more secure setting without fear of breaching privacy and confidentiality.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".