MétaCan
Menu
Back to cohort
Record W2611934960 · doi:10.5539/gjhs.v9n8p21

Crafting, Constructing and Developing a Nurses’ Professional Identity Scale (NPIS)

2017· article· en· W2611934960 on OpenAlexvenueno aff
Shehnaaz Moola

Bibliographic record

VenueGlobal Journal of Health Science · 2017
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)Scale (ratio)CategorizationPsychologyPsychological interventionPresentation (obstetrics)Factor (programming language)Self-conceptNursingSocial psychologyApplied psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.003
Open science0.0010.000
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 head0.050
GPT teacher head0.439
Teacher spread0.389 · 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.

Study designObservational
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

Citations5
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health ScienceSame topicNursing Education, Practice, and LeadershipFrench-language works237,207