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Record W1970225718 · doi:10.1002/nur.20383

Nurse burnout and quality of care: Cross‐national investigation in six countries

2010· article· en· W1970225718 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch in Nursing & Health · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsToronto General HospitalUniversity Health NetworkUniversity of TorontoRoyal Bank of Canada
FundersNational Institute of Nursing Research
KeywordsBurnoutNursingQuality (philosophy)MedicineLogistic regressionFamily medicinePsychologyClinical psychology

Abstract

fetched live from OpenAlex

We explored the relationship between nurse burnout and ratings of quality of care in 53,846 nurses from six countries. In this secondary analysis, we used data from the International Hospital Outcomes Study; data were collected from 1998 to 2005. The Maslach Burnout Inventory and a single-item reflecting nurse-rated quality of care were used in multiple logistic regression modeling to investigate the association between nurse burnout and nurse-rated quality of care. Across countries, higher levels of burnout were associated with lower ratings of the quality of care independent of nurses' ratings of practice environments. These findings suggest that reducing nurse burnout may be an effective strategy for improving nurse-rated quality of care in hospitals.

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.

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)0.016
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.194
GPT teacher head0.603
Teacher spread0.410 · 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