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Record W2951839764 · doi:10.1136/bmjqs-2018-009147

Safety-I, Safety-II and burnout: how complexity science can help clinician wellness

2019· article· en· W2951839764 on OpenAlexaff
Andrew Smaggus

Bibliographic record

VenueBMJ Quality & Safety · 2019
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsQueen's University
Fundersnot available
KeywordsBurnoutMedicinePatient safetyWorkplace safetyMedical educationNursingApplied psychologyOccupational safety and healthHealth careClinical psychologyPsychologyPathology

Abstract

fetched live from OpenAlex

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 …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.068
GPT teacher head0.407
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations63
Published2019
Admission routes1
Has abstractyes

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