Perceptions of Unprofessional Social Media Behavior Among Emergency Medicine Physicians
Bibliographic record
Abstract
ABSTRACT Background Use of social media (SM) by physicians has exposed issues of privacy and professionalism. While guidelines have been created for SM use, details regarding specific SM behaviors that could lead to disciplinary action presently do not exist. Objective To compare State Medical Board (SMB) directors' perceptions of investigation for specific SM behaviors with those of emergency medicine (EM) physicians. Methods A multicenter anonymous survey was administered to physicians at 3 academic EM residency programs. Surveys consisted of case vignettes, asking, “If the SMB were informed of the content, how likely would they be to initiate an investigation, possibly leading to disciplinary action?” (1, very unlikely, to 4, very likely). Results were compared to published probabilities using exact binomial testing. Results Of 205 eligible physicians, 119 (58%) completed the survey. Compared to SMB directors, EM physicians indicated similar probabilities of investigation for themes involving identifying patient images, inappropriate communication, and discriminatory speech. Participants indicated lower probabilities of investigation for themes including derogatory speech (32%, 95% confidence interval [CI] 24–41 versus 46%, P < .05); alcohol intoxication (41%, 95% CI 32–51 versus 73%, P < .05); and holding alcohol without intoxication (7%, 95% CI 3–13 versus 40%, P < .05). There were no significant associations with position, hospital site, years since medical school, or prior SM professionalism training. Conclusions Physicians reported a lower likelihood of investigation for themes that intersect with social identity, compared to SMB directors, particularly for images of alcohol and derogatory speech.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".