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Record W4412695724 · doi:10.1080/0142159x.2025.2533403

In gossip, we trust: Residents’ understanding of gossip as a social resource

2025· article· en· W4412695724 on OpenAlexaff
Laura Chiel, Michael D. Fishman, Lorelei Lingard, Erik W. Driessen, Emmaline Brouwer

Bibliographic record

VenueMedical Teacher · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsWestern University
Fundersnot available
KeywordsGossipResource (disambiguation)PsychologyEtiquetteSocial psychologyMedical educationComputer scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

PURPOSE: Gossip is pervasive in residency programs and may play a key role in resident development. In this study, conducted from a constructivist vantage point, we aim to explore residents' experience of gossip in the residency workplace. MATERIALS AND METHODS: Constructivist grounded theory was used to iteratively conduct and analyze interviews with 16 resident participants from pediatric, internal medicine, obstetrics-gynecology, and psychiatry programs located in the United States and the Netherlands. Interview questions focused on residents' personal experiences with gossip in training. RESULTS: We found that gossip has multiple emotional impacts on participants, while also helping them navigate the learning environment. Gossip participation itself must be navigated, but how participants do so varies, with each following a different map, or unspoken rules surrounding gossip etiquette, often routed by social connectivity and trust. CONCLUSIONS: We theorize gossip as a social resource in residency training. Gossip influences residents' emotions and, through gossip, residents learn what is expected of them and from others. However, gossip is not uniformly available. Residents likely experience differential emotional support and requisite information gained through gossip. Program leaders should be aware of the influence of gossip and seek to understand social connectivity in their programs.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.526
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.360
Teacher spread0.319 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations6
Published2025
Admission routes1
Has abstractyes

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