Safety-I, Safety-II and burnout: how complexity science can help clinician wellness
Bibliographic record
Abstract
The current crisis of clinician burnout is a complex problem. As rates of burnout (the workplace syndrome consisting of emotional exhaustion, depersonalisation and loss of meaning) reach disturbing levels among clinicians,1–3 we continue to struggle to understand how to address workplace suffering.4 5 An underexamined area of burnout is how the increasing complexity of healthcare, combined with our tentative recognition of complexity science (the study of systems governed by interactions, dependencies and relationships),6 impacts the well-being of clinicians. Complex sociotechnical systems present unique challenges for front-line clinicians and healthcare administrators. At the front lines, clinicians must navigate dynamic, unpredictable challenges and trade-offs. At the organisational level, complex systems do not respond predictably to improvement efforts. Due to their emergent properties, non-linearity and dense web of interactions, complex systems defy mechanistic thinking and formal rationality (ie, rationality based on bureaucratic rules, regulations and laws).7–11 The pursuit of safety and quality in healthcare has relied heavily on mechanistic thinking and formal rationality.12–14 This breeds an approach—labelled Safety-I—that conceptualises safety as the absence of failure, and suggests that safety and quality are best achieved via efforts to minimise performance variation and maximise compliance with idealised designs of work (through standardisation, regulation and measurement).8 While Safety-I has been a dominant paradigm within healthcare,12–14 its limitations for addressing the challenges presented by complex systems are leading some to argue that a paradigm shift is necessary to manage contemporary systems.7 12 13 Given these concerns and circumstances, we should consider whether such a paradigm shift could help us better understand and address clinician burnout. The ongoing dominance of Safety-I logic in an increasingly complex healthcare system may perpetuate a view of front-line work that does not reflect current realities and overlooks the challenges exhausting contemporary clinicians. Safety-II, a …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".