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Record W4206940855 · doi:10.1177/08445621221074872

New Nurses’ Perceptions on Transition to Practice: A Thematic Analysis

2022· article· en· W4206940855 on OpenAlexaffvenueabout
Amy Hallaran, Dana Edge, Joan Almost, Deborah Tregunno

Bibliographic record

VenueCanadian Journal of Nursing Research · 2022
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsQueen's UniversityTrent University
Fundersnot available
KeywordsThematic analysisWorkforceFeelingFacilitatorNursingTransition (genetics)PsychologyFocus groupQualitative researchMedicineMedical educationSocial psychologySociologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: New nurses' transition to the workforce is often described as challenging and stressful. Concerns over this transition to practice are well documented, with the hypothesis that transition experiences influence the retention of new nurses in the workforce and profession. METHODS: = 217) to assess new nurse transition in the province of Ontario, Canada, an open-ended item was included to solicit specific examples of the transition experience. The comments underwent thematic analysis to identify the facilitators and barriers of transition to practice for new nurses. RESULTS: Comments were provided by 196 respondents. Three facilitator themes (supportive teams; feeling accepted, confident, and prepared; new graduate guarantee) and four barrier themes (feeling unprepared; discouraging realities and unsupportive cultures; lacking confidence/feeling unsure; false hope) to new nurse transition emerged. CONCLUSIONS: Concerns of nursing shortages are heightened in the current COVID-19 pandemic, reinforcing the priority of retaining new nurses in the workforce. The reported themes offer insight into the contribution of a supportive work environment to new nurses' transition. The recommendations focus on aspects of supportive environments and educational strategies, including final practicums, to assist nursing students' development of self-efficacy and preparation for the workplace.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.743
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.459
Teacher spread0.379 · 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 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

Citations109
Published2022
Admission routes3
Has abstractyes

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