Development of a professional practice competency for undergraduate nursing students: A mixed-method study
Bibliographic record
Abstract
Nursing professionalism relates to the knowledge, skills, conduct, behaviour and attitudes of registered nurses. Difficulties related to student assessment of professionalism have been anecdotally described as a disparity between the meaning of the term ‘professionalism’ to nurses and its application and measurement in clinical practice. The aim of this study was to develop a professional practice competency for undergraduate nurses on clinical placement. An exploratory sequential mixed-methods approach with a two-phase design was used to develop the competency. Phase one, the Delphi method with 16 expert nurses, was used to develop the competency. This involved the thematic analysis of key statements over four rounds, five key themes were identified (attitudes, communication, knowledge, standards, relationships) that formed the framework and 33 individual competency statements. This was followed by phase 2, content validity, using the Table of Specification with 58 clinical facilitators. The findings confirmed the statements (80% consensus) deemed important to assess the essential construct of nursing professional practice. It is envisaged that the professional practice competency will assist student insight into their professional role and subsequently allow adjustment and achievement of professional practice competency.
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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 | 0.027 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".