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Record W2896253310 · doi:10.17483/2368-6669.1154

Becoming a Nurse: Student Experience of Transformation and Professional Identity

2018· article· en· W2896253310 on OpenAlexaffvenueabout
Pamela M. Nordstrom, Genevieve Currie, Shirley Meyer

Bibliographic record

VenueQuality Advancement in Nursing Education - Avancées en formation infirmière · 2018
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsMount Royal UniversityAmbrose University
Fundersnot available
KeywordsBachelorThematic analysisNurse educationNursingContext (archaeology)PsychologyPopulationPedagogyMedical educationQualitative researchMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

Nursing education programs are designed to respond to the evolving requirements of nursing practice while supporting student transformation in becoming a nurse. Students in these programs often refer to them as academically challenging and stressful. The aim of this study was to understand the experience of nursing students compared to the general university student population and specifically, to explore if nursing students are perceiving more stress than students in other university programs. This study arises from an earlier study conducted annually for 4 years at a western Canadian university following the method referred to as the Harvard Assessment Seminar (Light, 2001). In the primary study, that is the study conducted annually for 4 years, random samples of university students were interviewed about their university experience; these samples included bachelor of nursing students. The study reported in this paper is a secondary analysis of the primary study’s data and explores the unique experience of nursing students within the context of the broader university community. A total of 358 semi-structured interviews comprised the data for the primary study. Of these interviews, 64 respondents (18%) were registered in the bachelor of nursing program. A directed content analysis technique was used to extend the existing research by using pre-existing coding categories and allowing other themes to emerge that appeared unique to nursing students. The overarching theme that emerged from the secondary data was “becoming a registered nurse (RN) through the experience of being a student.” Three thematic clusters characterized the undergraduate nursing student’s journey of transformation to becoming a registered nurse: learning through experience; recognizing the link between theory and practice; and transition and transformation. A key finding of the study is that the nursing student experience is fundamental to becoming a registered nurse. Through learning and practice experiences a transformation in personal identity takes place. Nursing faculty can support learning through experiences that foster transition and transformation. Nursing students experience stress. The difference between nursing students and undergraduate students in general is the focus of their stress: volume of work, readiness for practice, and their capacity, capability, and worthiness to fulfill the nursing role, along with their perceptions of balancing their study–work–personal life. The direct measurement of the amount of stress in nursing students and undergraduate students in general was not possible. This research study will add to the literature by describing the student nursing experience in undergraduate education as compared to the general university population. In addition, the authors provide recommendations for nurse educators that support successful student transition to becoming a nurse.

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)0.006
metaresearch head score (Gemma)0.009
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 score0.008
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0070.004
Open science0.0010.010
Research integrity0.0020.004
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 head0.033
GPT teacher head0.449
Teacher spread0.416 · 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

Citations2
Published2018
Admission routes3
Has abstractyes

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