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Work‐Related Disability in Canadian Nurses

2004· article· en· W1975052202 on OpenAlexaffabout
Linda O’Brien‐Pallas, Judith Shamian, Donna Thomson, Christine Alksnis, Mieke Koehoorn, Michael Kerr, Shirliana Bruce

Bibliographic record

VenueJournal of Nursing Scholarship · 2004
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsInstitute of Health Services and Policy ResearchHealth CanadaInstitute for Work & HealthSt. Peter's HospitalUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsOvertimeQuartileOddsMedicineLogistic regressionDescriptive statisticsSick leaveOdds ratioCross-sectional studyNursingFamily medicineDemographyPhysical therapy

Abstract

fetched live from OpenAlex

PURPOSE: To determine factors contributing to high registered nurse (RN) injury claim rates in Canadian hospitals. DESIGN: Cross-sectional study of secondary 1998-99 data for RNs (N = 8,044) in Ontario, Canada, linked at the hospital level (n = 127). METHODS: Descriptive statistics, correlations, and logistic regression analyses were conducted. RESULTS: The odds of a high RN lost-time claim rate increased by 70% for each quartile increase in the percentage of RNs reporting more than 1 hour of overtime per week. The odds of a high RN musculoskeletal lost-time claim rate decreased by 64% for every one unit increase in the hospital-level score on the nurse-physician relationship subscale. CONCLUSIONS: To retain and optimize scarce hospital nursing resources, strategies to address overtime, sick time, and nurse-physician relationships might provide fiscal and human benefits.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.495

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 studies0.0000.000
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 head0.025
GPT teacher head0.344
Teacher spread0.319 · 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

Citations57
Published2004
Admission routes2
Has abstractyes

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