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Record W4390415930 · doi:10.1093/occmed/kqad112

Advancing fatigue management in healthcare: risk-based approaches that enhance health service delivery

2023· article· en· W4390415930 on OpenAlexafffund
Madeline Sprajcer, Amy Robinson, Matthew J. W. Thomas, Drew Dawson∥

Bibliographic record

VenueOccupational Medicine · 2023
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersRoyal College of Physicians and Surgeons of Canada
KeywordsWork (physics)Health careRisk managementBusinessControl (management)Service (business)Occupational safety and healthMedicineRisk analysis (engineering)MarketingEngineeringEconomicsFinanceManagement

Abstract

fetched live from OpenAlex

Given the need for 24/7 healthcare services, fatigue is an inevitable consequence of work in this industry. A significant body of regulatory advice and hospital services have focused primarily on restricting work hours as the primary method of mitigating fatigue-related risk. Given the inevitability of fatigue, and the limited capacity of labour agreements to control risk, this commentary explores how the principles of fatigue risk management might be applied in a healthcare setting.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.110
GPT teacher head0.382
Teacher spread0.272 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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