In Gossip, We Trust: Residents’ Understanding of Gossip as a Social Resource
Bibliographic record
Abstract
Abstract Introduction Gossip is pervasive in residency programs and may play a key role in resident development. Recommendations for how to deal with harmful gossip in residency programs have been proposed but, before addressing gossip, we need to more fully understand how gossip in residency programs functions as a social process and explore residents’ experiences with gossip across contexts. In this study, conducted from a constructivist vantage point, we aim to explore residents’ experience of gossip in the residency workplace. 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. Discussion We conceptualize 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. Because such support and information are critical in residency, program leaders should be aware of the influence of gossip and seek to understand social connectivity in their programs.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".