Defining Potentially Unprofessional Behavior on Social Media for Health Care Professionals: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Social media presence among health care professionals is ubiquitous and largely beneficial for their personal and professional lives. New standards are forming in the context of e-professionalism, which are loosening the predefined older and offline terms. With these benefits also come dangers, with exposure to evaluation on all levels from peers, superiors, and the public, as witnessed in the #medbikini movement. OBJECTIVE: The objectives of this study were to develop an improved coding scheme (SMePROF coding scheme) for the assessment of unprofessional behavior on Facebook of medical or dental students and faculty, compare reliability between coding schemes used in previous research and SMePROF coding scheme, compare gender-based differences for the assessment of the professional content on Facebook, validate the SMePROF coding scheme, and assess the level of and to characterize web-based professionalism on publicly available Facebook profiles of medical or dental students and faculty. METHODS: A search was performed via a new Facebook account using a systematic probabilistic sample of students and faculty in the University of Zagreb School of Medicine and School of Dental Medicine. Each profile was subsequently assessed with regard to professionalism based on previously published criteria and compared using the SMePROF coding scheme developed for this study. RESULTS: Intercoder reliability increased when the SMePROF coding scheme was used for the comparison of gender-based coding results. Results showed an increase in the gender-based agreement of the final codes for the category professionalism, from 85% in the first phase to 96.2% in the second phase. Final results of the second phase showed that there was almost no difference between female and male coders for coding potentially unprofessional content for students (7/240, 2.9% vs 5/203, 2.5%) or for coding unprofessional content for students (11/240, 4.6% vs 11/203, 5.4%). Comparison of definitive results between the first and second phases indicated an understanding of web-based professionalism, with unprofessional content being very low, both for students (9/222, 4.1% vs 12/206, 5.8%) and faculty (1/25, 4% vs 0/23, 0%). For assessment of the potentially unprofessional content, we observed a 4-fold decrease, using the SMePROF rubric, for students (26/222, 11.7% to 6/206, 2.9%) and a 5-fold decrease for faculty (6/25, 24% to 1/23, 4%). CONCLUSIONS: SMePROF coding scheme for assessing professionalism of health-care professionals on Facebook is a validated and more objective instrument. This research emphasizes the role that context plays in the perception of unprofessional and potentially unprofessional content and provides insight into the existence of different sets of rules for web-based and offline interaction that marks behavior as unprofessional. The level of e-professionalism on Facebook profiles of medical or dental students and faculty available for public viewing has shown a high level of understanding of e-professionalism.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".