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Record W4385514921 · doi:10.1111/inr.12868

Trainee nursing associates in England: A multisite qualitative study of higher education institution perspectives

2023· article· en· W4385514921 on OpenAlexfundno aff
Steve Robertson, Rachel King, Bethany Taylor, Sara Laker, Emily Wood, Michaela Senek, Angela Tod, Tony Ryan

Bibliographic record

VenueInternational Nursing Review · 2023
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsnot available
FundersRéseau de cancérologie Rossy
KeywordsThematic analysisWorkforceNursingNurse educationGovernment (linguistics)Nursing shortageQualitative researchPsychologyMedical educationPolitical sciencePedagogySociologyMedicine

Abstract

fetched live from OpenAlex

AIM: To explore the experiences of university employees on the development and implementation of the nursing associate programme. BACKGROUND: As part of wider policy initiatives to address workforce shortages, provide progression for healthcare assistants and offer alternative routes into nursing, England recently introduced the nursing associate level of practice. Little research has yet considered university perspectives on this new programme. METHODS: An exploratory qualitative study reported following COREQ criteria. Twenty-seven university staff working with trainee nursing associates in five universities across England were recruited. Data, collected via semi-structured interviews from June to September 2021, were analysed through a combined framework and thematic analysis. RESULTS: Three themes developed: 'Centrality of partnerships' considered partnerships between employers and universities and changing power dynamics. 'Adapting for support' included responding to new requirements and changing pedagogical approaches. 'Negotiating identity' highlighted the university's role in advocacy and helping trainees develop a student identity. CONCLUSIONS: Nursing associate training in England has changed the dynamics between universities and healthcare employers, shifting learners' identity more to 'employee' rather than 'student'. Universities have adapted to support trainees in meeting academic and professional standards whilst also meeting employer expectations. While challenges remain, the ability of nurse educators to make adjustments, alongside their commitment to quality educational delivery, is helping establish this new training programme and thereby meet government policy initiatives. IMPLICATIONS FOR NURSING POLICY: The international movement of apprenticeship models in universities has the potential to change the status of the learner in nursing educational contexts. National policies that encourage this model should ensure that the implications and challenges this change of status brings to learners, employers and education institutions are fully considered prior to their implementation.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.379
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.047
GPT teacher head0.457
Teacher spread0.411 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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