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Record W2940022033 · doi:10.4300/jgme-d-19-00119.1

Approaching Gossip and Rumor in Medical Education

2019· letter· en· W2940022033 on OpenAlexaff
Michael Chaikof, Evan Tannenbaum, Siddhi Mathur, Janet Bodley, Michèle Farrugia

Bibliographic record

VenueJournal of Graduate Medical Education · 2019
Typeletter
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsGossipRumorContext (archaeology)PsychologyConstructiveSocial psychologyComputer sciencePublic relationsPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.048
GPT teacher head0.380
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations8
Published2019
Admission routes1
Has abstractyes

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