Social Media and the Transformation of the Physician-Patient Relationship: Viewpoint
Bibliographic record
Abstract
As many as 80% of internet users seek health information online. The social determinants of health (SDoH) are intimately related to who has access to the internet and health care as a whole. Those who face more barriers to care are more likely to benefit from accessing health information online, assuming the information they are retrieving is accurate. Virtual communities on social media platforms are beginning to serve as venues for seeking health information online because peers have been shown to influence health behavior more than almost anything else. As a positive mediator of health, social media can be used as a direct or indirect mode of communication between physicians and patients, a venue for health promotion and health information, and a community support network. However, false or misleading content, social contagion, confirmation bias, and security and privacy concerns must be mitigated to realize the full potential of social media as a positive mediator of health. This paper presents the shifting dynamics of how such communities are affecting physician-patient relationships. With the intersections between the SDoH, social media, and health evolving, physicians must take into consideration these factors when establishing their relationships with patients. We argue a paradigm shift in the physician-patient relationship is warranted, one where physicians acknowledge the impacts of the SDoH on information-seeking behavior, recognize the positive and negative roles of social media as a mediator of health through the lens of the SDoH, and use social media to catalyze positive changes in the physician-patient relationship. We discuss how the physician-patient relationship must evolve to accommodate for the ever-increasing role of social media in health and to best use social media as a tool to improve health outcomes. Finally, we present a fluid and multicomponent diagram that we believe will assist in framing future research in this area. We conclude that it is ineffective and even counterproductive for physicians to ignore the relationship between social media, the SDoH and health, their impact on one another, and the effect it has on designing the medical encounter and the delivery of care under the definition of precision medicine.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".