A Qualitative Study of Workplace Gossip in the Residency Learning Environment
Bibliographic record
Abstract
Background Gossip is a ubiquitous sociocultural phenomenon serving many functions in human interactions, including in workplace and academic settings. Gossip can have profound positive and negative impacts; however, its impact on medical residents and their learning environment is unknown. Objective To understand the function and impact of workplace gossip—from and about colleagues and supervisors—on medical residents’ experiences in their learning environment. Methods This exploratory study used principles of constructivist grounded theory. Data were collected from residents in the obstetrics and gynecology and psychiatry programs at a large academic institution via semistructured interviews in 2021. Seven residents were interviewed. Transcripts were analyzed using inductive coding, and themes were identified. Results Thematic analysis revealed perceived positive and negative impacts of gossip. Perceived positive impacts included affirmation and roadmapping. Perceived negative impacts included anxiety/fear and the role of gossip in reinforcing the educational hierarchy. Other themes, which are perceived to have both positive and negative impacts, included the role of gossip in learning and in group status. The identity of the gossiper, audience, and subject of gossip also impacted its effects. Gossip between residents about faculty members was perceived as roadmapping, or an affirming role, whereas gossip involving faculty members created anxiety and reinforced negative aspects of the hierarchy. Conclusions Workplace gossip serves both positive and negative roles within the medical learning environment. It can function as informal debriefing and can also threaten psychological safety. Its impact on residents is affected by the identities of the gossiper, subject, and audience.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".