Organizational Factors Contributing to Incivility at an Academic Medical Center and Systems-Based Solutions: A Qualitative Study
Bibliographic record
Abstract
PURPOSE: A rise in incivility has been documented in medicine, with implications for patient care, organizational effectiveness, and costs. This study explored organizational factors that may contribute to incivility at one academic medical center and potential systems-level solutions to combat it. METHOD: The authors completed semistructured individual interviews with full-time faculty members of the Department of Medicine (DOM) at the University of Toronto Faculty of Medicine, Toronto, Ontario, Canada, with clinical appointments at six affiliated hospitals, between June and September 2016. They asked about participants' experiences with incivility, potential contributing factors, and possible solutions. Two analysts independently coded a portion of the transcripts until a framework was developed with excellent agreement within the research team, as signified by the Kappa coefficient. A single coder completed analysis of the remaining transcripts. RESULTS: Forty-nine interviews with physicians from all university ranks and academic position descriptions were completed. All participants had collegial relationships with colleagues but had observed, heard of, or been personally affected by uncivil behavior. Incivility occurred furtively, face-to-face, or online. The participants identified several organizational factors that bred incivility including physician nonemployee status in hospitals, silos within the DOM, poor leadership, a culture of silence, and the existence of power cliques. They offered many systems-level solutions to combat incivility through prevention, improved reporting, and clearer consequences. CONCLUSIONS: Existing strategies to combat incivility have focused on modifying individual behavior, but opportunities may exist to reduce incivility through a greater understanding of the role of health care organizations in shaping workplace culture.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| 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".