On Surgeon Stress: An Exploration of Distress and Eustress
Bibliographic record
Abstract
Rationale: High rates of distress among surgeons, and physicians more broadly, are alarming and negatively impacting provider wellness, performance, and healthcare system functioning. Yet despite increased attention, we continue to struggle in addressing stress in practice. One challenge is the lack of a shared vocabulary for understanding stress, which is a complex and idiosyncratic phenomenon that can be experienced both positively (as eustress) and negatively (as distress). While traditional reductionist approaches focusing on individual facets –such as physiology or cognition– have greatly advanced scientific knowledge, stepping back to examine the composite, subjective experience of stress may provide new, complementary insights for mitigating distress and supporting more favorable states in practice. Objectives: This research program explored the multidimensional, integrated experience of stress among academic surgeons in order to establish conceptual frameworks for understanding surgeon distress and eustress in practice. Design: Qualitative data were collected over three phases, guided by a constructivist grounded theory methodology. Semi-structured interviews were conducted with staff surgeons affiliated with the University of Toronto, purposively sampled to capture a broad range of perspectives including specialty and experience level. Results: Eustress and distress were reconceptualized as multidimensional experiences, comprising physiologic, physical, cognitive, affective, social, cultural, and environmental facets. Subjective control was identified as central to the stress experience. In contrast to traditional dichotomous views placing eustress and distress on two polar ends of a spectrum, findings illustrated how experiences of distress and eustress could overlap and coexist. Conceptual frameworks were established for understanding eustress and distress, and how physicians might reflect on their own experiences in practice. Conclusions: Stress is not simply positive or negative, cognitive or physiologic, or individual- versus system-based; understanding the points of intersection and seeking to embrace inherent complexities in the subjective human experience may provide deeper understandings and allow us to approach stress in a more authentic way.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".