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Record W4236890826 · doi:10.12927/cjnl.2010.21597

Canadian Oncology Nurse Work Environments: Part I

2010· article· en· W4236890826 on OpenAlexafffundvenueabout
Debra Bakker, J. Michael Conlon, Margaret I. Fitch, Esther Green, Lorna Butler, Kärin Olson, Greta G. Cummings

Bibliographic record

VenueNursing leadership · 2010
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsLaurentian University
FundersCanadian Institutes of Health Research
KeywordsOncology nursingWork (physics)NursingNurse AdministratorMedicineMEDLINENurse educationPsychologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

The global nursing shortage and statistics indicating a steady increase in the cancer patient workload suggest that the recruitment and retention of oncology nurses is and will be a serious problem. The purpose of this research study was to examine oncology nursing work environments in Canada and to determine the presence of workplace and professional practice factors. A total of 615 oncology nurses responded to a national survey in 2004. The majority of nurses indicated that positive nurse-physician relations and autonomy in clinical decision-making were factors that contributed to job satisfaction and the desire to remain in oncology nursing. However, the findings identified that nurse staffing, the lack of nursing leadership and inadequate opportunities to participate in policy decisions were areas of concern. Differences in work environment perceptions were seen most often when responses were compared across provincial regions. While the findings support previous research reports that the key to the nursing shortage is attention to nursing work environments, they also emphasize the need for organizations to act now. A follow-up survey was conducted in 2006; analysis of these data will be presented in a future report on nurses' perceptions of their work environments and job satisfaction over time.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.761
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.005

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 head0.138
GPT teacher head0.390
Teacher spread0.252 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations12
Published2010
Admission routes4
Has abstractyes

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