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Record W3147667838 · doi:10.1002/nop2.868

A survey of nurses' experience integrating oncology clinical and academic worlds

2021· article· en· W3147667838 on OpenAlexaff
Kristen R. Haase, Fay J. Strohschein, Tara C. Horill, Leah K. Lambert, Tracy Powell

Bibliographic record

VenueNursing Open · 2021
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsMount Royal UniversityUniversity of ManitobaUniversity of CalgaryUniversity of British Columbia
Fundersnot available
KeywordsMedical educationWork (physics)Clinical PracticeExploratory researchGraduate studentsQuality (philosophy)PsychologyMedicineNursingSociology

Abstract

fetched live from OpenAlex

AIM: To better understand how oncology nurses (a) navigate graduate studies; (b) perceive the impact of their academic work on their clinical practice, and vice versa; and (c) engage with clinical settings following graduate work. DESIGN: Interpretive descriptive cross-sectional survey. METHODS: A qualitative exploratory web-based survey exploring integration of graduate studies and clinical nursing practice. RESULTS: About 87 participants from seven countries responded. 71% were employed in clinical settings, 53% were enrolled in/graduated from Master's programs; 47% were enrolled in/graduated from doctoral programs. Participants had diverse motivations for pursuing graduate studies and improving clinical care. Participants reported graduate preparation increased their ability to provide quality care and conduct research. Lack of time and institutional structures were challenges to integrating clinical work and academic pursuits. CONCLUSIONS: Given the many constraints and numerous benefits of nurses engaging in graduate work, structures and strategies to support hybrid roles should be explored.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.180
GPT teacher head0.525
Teacher spread0.344 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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