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Record W2017655432 · doi:10.1002/nur.20349

Modeling influences on acute care nurses' engagement in tobacco use reduction

2009· article· en· W2017655432 on OpenAlexafffundabout
Annette Schultz, Shahadut Hossain, Joy L. Johnson

Bibliographic record

VenueResearch in Nursing & Health · 2009
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsOccupational Cancer Research CentreUniversity of British ColumbiaUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsTobacco useLeverage (statistics)Perspective (graphical)MedicinePsychologyNursingEnvironmental health

Abstract

fetched live from OpenAlex

Although nurses are encouraged to address patients' tobacco use, the integration of tobacco reduction into practice has not been consistent. An organizational behavior perspective was used to conceptualize hypothesized relationships among reported influencing factors (individual characteristics, role attitudes, perceived barriers, and workplace climate). Survey data collected at two Western Canadian hospitals (N = 214 nurses; 58% response) were used to test the model. The final model explained nearly 60% of variation in the nurses' tobacco reduction practice. Role attitude, perceived resource availability, co-worker's activities, and ability were the strongest contributors. Nurses' smoking status indirectly influenced practice through shaping role attitudes and perceived ability. Diverse leverage points to enhance nurses' involvement in patients' tobacco use were identified.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.170
GPT teacher head0.522
Teacher spread0.351 · 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.

Study designOther design
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

Citations15
Published2009
Admission routes3
Has abstractyes

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