Defining Potentially Unprofessional Behavior on Social Media for Health Care Professionals: Mixed Methods Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».