Crafting, Constructing and Developing a Nurses’ Professional Identity Scale (NPIS)
Bibliographic record
Abstract
The main objective was to measure the professional identity of nurses and to evaluate the ways to measure and develop the Nurse’s Professional Identity Scale (NPIS) as perceived by Saudi student nurses. The study employed a quantitative research design to assess the measurement scale of Nurse’s Professional Identity. Data collection was done through a questionnaire from 442 student nurses, who have been recruited through a randomized sampling approach. A factor analysis identified five-factor dimensions within a multi-dimensional structure of 45 items. Factor 1 has been identified as the most important factor on self-presentation as most significant and important to the technique of constructing and forming a professional identity. Factor 2 has accounted for 5.62; Factor 3 has accounted for 5.14; Factor 4 has accounted for 4.29; and Factor 5 has accounted for 4.25. Factor 1 consisted of 16 variables and all items with loadings greater than (>0.3), which deals with self-esteem. It has been evaluated that the nursing professional identity scale can be used to adapt and assess the developing/forming stages of student nurses and the variables needed for constituting a professional identity. Self-presentation, self-image, self-esteem, self-categorization and self-concept are directly associated with certain activities, interventions, and approaches required to be developed.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".