Development and Validation of a Tool for Measuring the Professional Identity of Nursing Students: The Q-IPEI
Bibliographic record
Abstract
Background: The various pressures on nurses in their practice environment, given the complexity of care, have exposed the confusion around the role definition and level of professional identity (PI) of future nurses. To support them in their practice, it is important throughout their education to know their commitment, representations, and sense of identity with respect to their future profession. To our knowledge, there is no measurement tool in the literature that can be used to measure the PI of nursing students throughout their education, given its complex multidimensional nature. Objective: Based on a framework that takes into account the diversity of meanings of PI, the aim of this study was to construct and validate a questionnaire, the Q-IPEI that describes the PI of future nursing students at the personal, relational, and professional practice levels. Methods: A development research approach was used. It was based on a literature review, expert consultation, and validation of the psychometric properties of the Q-IPEI. Data were collected from 488 nursing students in 2013 and 504 in 2014 at five levels of nursing education in the province of Quebec, Canada. Results: The validated Q-IPEI is a questionnaire divided into three components, 11 dimensions, and 68 items. Its factor structure was explored through principal component analyses using 2013 data. It was then confirmed with 2014 data, with an RMSEA of 0.072 and CFI of 0.861. Internal consistency was considered acceptable with a Cronbach’s alpha of 0.823 in 2013 and 0.832 in 2014. Discussion: Using the Q-IPEI, key decision-makers in the health system would serve to consolidate and develop proactive strategies with nursing students throughout their education to strengthen their confidence as future nurses and especially help them cope with the current challenges facing the nursing profession.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".