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Record W2804497717 · doi:10.1177/0844562118771832

“Theory Becoming Alive”: The Learning Transition Process of Newly Graduated Nurses in Canada

2018· article· en· W2804497717 on OpenAlexaffvenueabout
Violeta Nour, Anne Williams

Bibliographic record

VenueCanadian Journal of Nursing Research · 2018
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsProcess (computing)Transition (genetics)Computer scienceEngineering ethicsEngineeringChemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Newly graduated nurses often encounter a gap between theory and practice in clinical settings. Although this has been the focus of considerable research, little is known about the learning transition process. PURPOSE: The purpose of this study was to explore the experiences of newly graduated nurses in acute healthcare settings within Canada. This study was conducted to gain a greater understanding of the experiences and challenges faced by graduates. METHODS: Grounded theory method was utilized with a sample of 14 registered nurses who were employed in acute-care settings. Data were collected using in-depth interviews. Constant comparative analysis was used to analyze data. RESULTS: Findings revealed a core category, "Theory Becoming Alive," and four supporting categories: Entry into Practice, Immersion, Committing, and Evolving. Theory Becoming Alive described the process of new graduate nurses' clinical learning experiences as well as the challenges that they encountered in clinical settings after graduating. CONCLUSIONS: This research provides a greater understanding of learning process of new graduate nurses in Canada. It highlights the importance of providing supportive environments to assist new graduate nurses to develop confidence as independent registered nurses in clinical areas. Future research directions as well as supportive educational strategies are described.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.397
Teacher spread0.328 · 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 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

Citations28
Published2018
Admission routes3
Has abstractyes

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