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Record W1967394085 · doi:10.12927/cjnl.2010.21598

"Hit the Ground Running": Perspectives of New Nurses and Nurse Managers on Role Transition and Integration of New Graduates

2010· article· en· W1967394085 on OpenAlexafffundvenue
Wanda M. Chernomas, W. Dean Care, Jo-Ann McKenzie, Lorna Guse, Jan Currie

Bibliographic record

VenueNursing leadership · 2010
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsThematic analysisContext (archaeology)NursingConstructiveConfusionTransition (genetics)PsychologyPublic relationsMedicineSociologyQualitative researchPolitical science

Abstract

fetched live from OpenAlex

The workplace for new graduates must be a constructive learning environment to facilitate their development. Nurse managers need new graduates who can "hit the ground running." Conflict between the needs of new nurses and the realities of the workplace often creates role confusion and tension in new graduates and threatens employers' ability to retain them. As part of a larger study that examined the effectiveness of a new strategy on new nurse retention and workplace integration, we conducted focus groups with new nurses and nurse managers. This paper discusses the perspectives of new nurses on their role transition from graduates to practising professionals and the perspectives of nurse managers on the workplace integration of new nurses. The thematic findings integrate new nurses' perspectives on their needs during role transition with the perspectives of nurse managers in meeting those needs. The discussion includes strategies to facilitate successful transition and integration of new nurses into the workplace within the context of recruitment and retention.

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)0.012
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0130.011
Scholarly communication0.0080.006
Open science0.0010.005
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.313
Teacher spread0.233 · 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 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

Citations34
Published2010
Admission routes3
Has abstractyes

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