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Nurses? perceptions of hospital work environments

2007· article· en· W1995230453 on OpenAlexaffabout
Linda M. Hall, Diane Doran

Bibliographic record

VenueJournal of Nursing Management · 2007
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of TorontoCanadian Institutes of Health Research
Fundersnot available
KeywordsNursingJob satisfactionAffect (linguistics)MedicineCross-sectional studyNursing managementPerceptionUnit (ring theory)Work (physics)Family medicinePsychology

Abstract

fetched live from OpenAlex

AIM: To examine factors within the nursing work environment that may affect nurse outcomes. BACKGROUND: Primary data were acquired from unit managers and staff nurses on the study units. Secondary data were collected from health records administrative databases. The sample included adult medical and surgical units within all 19 teaching hospitals in Ontario, Canada. METHODS: A cross-sectional study design was employed in this study. A random sampling process was used to recruit the number of nurses (n = 1,116) required to provide a statistically adequate sample for the survey. RESULTS: Perceptions of the quality of care at the unit level were found to have a statistically significant positive influence on nurses' job satisfaction, and a statistically significant negative influence on nurses' job pressure and job threat. CONCLUSIONS: The results of this study underscore the importance of examining the environment in which nurses' work as a potential factor that influences outcomes experienced by patients and nurses.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.310
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations93
Published2007
Admission routes2
Has abstractyes

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