Approaching Gossip and Rumor in Medical Education
Notice bibliographique
Résumé
Dr. Mariam Rahmani's recent perspectives article in the Journal of Graduate Medical Education provided a straightforward approach to helping program directors manage rumors and gossip.1 We were pleased to see that this important topic is making its way into the medical education literature, especially as social media and other electronic communication methods make the transmission of rumors increasingly effortless. While Dr. Rahmani presents a concrete framework for dealing with rumors, we argue that there is an important distinction between rumor and workplace gossip that must be clarified.Gossip is different from rumor. Gossip has recently been defined as “evaluative talk about a person who is not present,” whereas a rumor is “an unconfirmed statement or report that is in widespread circulation.”2 These are related because they often involve the discussion of a person's behavior who is not present, but there are important differences between them. Rumors are almost always speculative, lacking in evidence and legitimacy. They are transmitted from person-to-person, not back and forth between individuals, as with gossip. Gossip is often transactional and is usually rooted in truth. While rumors are predominantly negative or harmful, gossip can be characterized differently.The literature has shown that gossip can be an important sociocultural tool, especially in the context of team formation, learning, and cooperation. Positive gossip can serve to establish constructive group norms. For example, gossiping with a fellow resident about another resident's strong surgical skills communicates that surgical skills are valued and likely to be praised. In experimental models, positive and negative gossip have been shown to influence overall group and individual performance.3 Gossip can be a low-stakes method of reinforcing positive in-group behaviors and deterring negative behaviors, by targeting those who demonstrate or violate group norms, respectively.Negative gossip, on the other hand, can be harmful within learning communities. Ellwardt and colleagues showed that workplace gossip can lead to a “scapegoating” phenomenon, where those with low status tend to be targets of negative gossip more often than high-status individuals.4 This behavior can lead to further marginalization of those individuals.Negative workplace gossip can also be a symptom of dysfunctional work environments. Kuo et al showed that employees are more likely to gossip about abusive supervisors and violations of the social contract,5 the informal set of rules that dictates how employees and employers are meant to conduct themselves and interact with one another.6 Therefore, in our context, the prevalence of negative gossip among residents can be an indication to program directors that violations (eg, harassment and bullying) are occurring in the workplace. In a sense, negative gossip may serve as an indicator of disruptive behavior in the workplace.In summary, gossip and rumors are related, yet distinct, phenomena. We believe that Dr. Rahmani has presented a meaningful framework for dealing with rumor, but we argue that management of gossip requires a nuanced approach. We must accept that gossip occurs in the workplace and work toward a better understanding of its role in medical education.
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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 | 0,015 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,002 |
| Études des sciences et des technologies | 0,010 | 0,020 |
| Communication savante | 0,011 | 0,018 |
| Science ouverte | 0,001 | 0,011 |
| Intégrité de la recherche | 0,011 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».