Professional Identity Formation: A Concept Analysis
Bibliographic record
Abstract
Purpose: Becoming and being a nurse is vaguely conceptualized, so it is important for nurses to understand the defining attributes, antecedents, consequences, model cases, and empirical referents of professional identity with the aim of better understanding the process of its formation. Method: Walker and Avant’s (2005) method is used to guide this concept analysis of professional identity. Findings: The analysis indicated that formation of professional identity in nursing consists of three main themes: individual characteristics, becoming a nurse, and professional identity. This article explores our understanding of the concept in relation to the current literature, and a research study conducted with new graduate nurses on their experiences of becoming and being nurses. Conclusions: An enhanced understanding of professional identity and its formation in the context of nursing practice could help to improve retention of new nurses, and address the academic-practice gap by informing how nursing education and practice settings can better prepare and support new graduates in becoming and being nurses. Implications: A model case, borderline case, and contrary case of professional identity are provided and supported by examples from new graduate nurses describing their experiences of becoming and being a nurse. Understanding professional identity can inform nursing education, policy, practice, and future research.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".