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Record W2067498530 · doi:10.12927/hcq.2009.20971

Building Clinical and Organizational Resilience to Reconcile Safety Threats, Tensions and Trade-Offs: Insights from Theory and Evidence

2009· article· en· W2067498530 on OpenAlexafffund
Lianne Jeffs, Deborah Tregunno, Kathleen MacMillan, Sherry Espin

Bibliographic record

VenueHealthcare Quarterly · 2009
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsSt. Michael's Hospital
FundersOntario Ministry of Health and Long-Term CareGovernment of OntarioCanadian Health Services Research Foundation
KeywordsHealth careResilience (materials science)Patient safetyWork (physics)BusinessPsychological resilienceRisk analysis (engineering)Process managementQuality (philosophy)Knowledge managementPublic relationsPsychologyComputer sciencePolitical scienceEngineeringSocial psychology

Abstract

fetched live from OpenAlex

Healthcare delivery settings are complex adaptive and tightly coupled, interrelated systems. Within the larger healthcare system, a key subsystem is the "clinical microsystem" level. It is at this level that clinicians are faced with high levels of uncertainty in their daily work - uncertainty that impacts the quality and safety of care that patients receive. The first aim of this paper is to enhance healthcare leaders' understanding of what is currently known about safety threats and strategies to manage the inherent tensions and trade-offs that occur in everyday practice. The second aim is to inform strategies that build clinical and organizational resilience through a multi-level framework derived from the collective theoretical and empirical work. Together, this information can strengthen safety practices throughout healthcare organizations.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.097
GPT teacher head0.442
Teacher spread0.345 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2009
Admission routes2
Has abstractyes

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