Graduate entry nursing students' development of professional nursing self: A scoping review
Bibliographic record
Abstract
Background: Accelerated graduate entry nursing programmes require students to rapidly socialise to the profession. Professional identity is an important element of becoming a nurse. Objective: This scoping review aimed to synthesise published literature reporting the development of professional identity, belongingness and self-concept as a nurse in students enrolled in a pre-registration graduate entry nursing programme. Design: Scoping review. Setting: Graduate entry nursing programmes. Participants: Graduate entry nursing students. Method: Following a pre-registered protocol, we searched electronic databases for publications investigating graduate entry nursing students' development of professional identity, belongingness and self-concept. Screening, data extraction and analysis were initially in duplicate and independent, and then by consensus. Results: Of the 871 records identified, twenty met the inclusion criteria. Publications were from the USA, Australia, New Zealand, Canada, and the UK. We identified one overarching theme of ‘professional nursing self’, with four sub-themes: 1) professional socialisation, 2) professional self-concept, 3) developing nursing agency, and 4) identity formation. Socialisation into nursing and belongingness to the profession occurred concurrently as students moved through their programme of learning. Due to the accelerated nature of the programmes, rapid professional socialisation was required, supported by positive relationships in the clinical setting. Strategies that enhanced belongingness and wellbeing enabled students to feel connected to the profession. Conclusions: The development of professional identity in graduate entry nursing students is impacted by their rapid professional transition through an accelerated programme. Students' growing sense of nursing agency is embodied in their experiences of thinking and acting as a nurse. Their previous professional identity is then reconstituted in their new graduate selves; educational programmes support this transition. Tweetable abstract: Scoping review finds professional identity development in graduate entry nursing students is rapid in accelerated preregistration degrees #belonging #connection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".