The Evolution of a Professional Identity as Perceived by Saudi Student Nurses
Bibliographic record
Abstract
The professional identity of student nurses may fluctuate or even disintegrate when exposed to clinical realities. A self-identity must be integrated firstly with new expectations and modified within a social context to form a professional identity. In the process of developing a professional identity, student nurses either develop a self-concept within a professional role based on attributes, beliefs, values, motives, experiences, morals and ideals of who and what a nurse is, or lack to develop in this role. This study targeted to investigate the perceptions of Saudi student nurses in the evolution of a professional identity. A non-probabilistic and descriptive approach was selected for data collection. A Nurses Professional Identity Scale was constructed by the researcher to explore the evolution of a professional identity as perceived by Saudi student nurses. Mean scores indicated the importance of the self-presentation, self-image, self-esteem, self-categorization and self-concept as dimensions, which facilitates a professional identity. The p-values obtained for all the factors were less than the level of significance (p-value<0.05), which indicated the importance of all the associated factors. The significance of how student nurses identify themselves as professionals during the Baccaulearate program in various role formation has been emphasized. These roles are still undervalued by society and influences stereotypical attitudes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".