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Record W1988117454 · doi:10.1177/0193945906287213

Teaching and Community Hospital Work Environments

2006· article· en· W1988117454 on OpenAlexaff
Linda M. Hall, Diane Doran, Souraya Sidani, Leah Pink

Bibliographic record

VenueWestern Journal of Nursing Research · 2006
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNursingWorkforceOrganizational cultureWork (physics)Job satisfactionCommunity hospitalUnit (ring theory)Acute careQuality (philosophy)MedicinePsychologyMedical educationHealth carePublic relationsPolitical science

Abstract

fetched live from OpenAlex

The Institute of Medicine report suggests that nursing work environments experience threats to patient safety related to organizational management and workforce deployment practices, work design, and organizational culture. Organizational factors contribute to nursing and potentially patient outcomes, yet few studies have examined the differences in practices perceived by nurses employed in different settings. Nurses from 16 medical and surgical units in eight randomly selected acute care hospitals representing teaching and community organizations participated in this project. Nurses working in teaching hospitals reported lower levels of role tension, yet their perceptions of the quality of work, the work environment, nursing unit leadership, quality of care, and levels of job stress and job satisfaction were higher than their colleagues in the community sites. This study highlights some important differences between teaching and community hospitals that can inform nurse executives and policy makers of the unique work-life issues for different groups of 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.065
GPT teacher head0.403
Teacher spread0.339 · 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 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

Citations16
Published2006
Admission routes1
Has abstractyes

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