‘My doctor self and my human self’: A qualitative study of physicians' presentation of self on social media
Bibliographic record
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, it is necessary 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 to inform such policies and resources. METHODS: Twenty-eight US-based physicians who use social media were interviewed. Participants were asked to describe how and why they use social media, specifically Twitter (rebranded as 'X' in 2023). Interviews were complemented by data from the participants' Twitter profiles. Data were analysed using reflexive thematic analysis guided by Goffman's dramaturgical model. This model uses the metaphor of a stage to characterise how individuals attempt to control the aspects of the identities-or faces-they display during social interactions. RESULTS: The participants presented six faces, which included professionally focused faces (e.g. networker) and those more personal in nature (e.g. human). The participants crafted and maintained these faces through discursive choices in their tweets and profiles, which were motivated by their audience's perceptions. We identified overlaps and tensions at the intersections of these faces, which posed professional and personal challenges for participants. CONCLUSIONS: Physicians strategically emphasise 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 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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".