“My doctor self and my human self”: A qualitative study of physicians’ presentation of self on social media
Bibliographic record
Abstract
Abstract Introduction When using social media, physicians are encouraged and trained to maintain separate professional and personal identities. However, this separation is difficult and even undesirable, as the blurring of personal and professional online presence can influence patient trust. Thus, to develop policies and educational resources that are more responsive to the blurring of personal and professional boundaries on social media, this study aims to provide an understanding of how physicians present themselves holistically online. Methods 28 physicians based in the United States that use social media were interviewed. Participants were asked to describe how and why they use social media, specifically Twitter (rebranded as “X” in July 2023), which is especially popular among physicians. Interviews were complimented by data from participants’ Twitter profiles. Data were analyzed using reflexive thematic analysis guided by Goffman’s theory of presentation of self. This theory uses the metaphor of a stage to characterize how individuals attempt to control the aspects of the identities—or faces— they display during social interactions. Results We identified seven faces presented by the participants. Participants crafted and maintained these faces through discursive choices in their tweets and profiles, which were motivated by their perceived audience. We identified overlaps and tensions that arise at the intersections of faces, which posed professional and personal challenges for participants. Conclusions Physicians strategically emphasize their more professional or personal faces according to their objectives and motivations in different communicative situations, and tailor their language and content to better reach their target audiences. While tensions arise in between these faces, physicians still prefer to project a rounded, integral image of themselves on social media. This suggests a need to reconsider social media policies and related educational initiatives to better align with the realities of these digital environments.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".