Understanding skill acquisition among registered nurses: the ‘perpetual novice’ phenomenon
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To determine whether the perpetual novice phenomenon exists beyond nephrology nursing where it was first described. BACKGROUND: The perpetual novice is a state in which nurses are unable to progress from a novice to an expert in one or more essential clinical skills which are used in their practice area. Maintaining clinical competence is essential to quality patient care outcomes. DESIGN: An exploratory, sequential, mixed methods design was used, comprised of a quantitative component followed by in-depth interviews. METHODS: Registered nurses employed in one of four roles were recruited from two university-affiliated hospitals in London, Ontario, Canada: Clinical Educator, Clinical Nurse Specialist, Advanced Practice Nurse and Nurse Practitioner. Participants were first asked to complete and return a survey and demographic questionnaire. Following the return of the completed surveys, ten participants were interviewed to enhance the results of the surveys. RESULTS: The results of the surveys confirmed that the perpetual novice phenomenon exists across multiple nursing care areas. Four contributing factors, both personal and structural in nature, emerged from the interviews: (1) opportunities for education, (2) the context of learning, (3) personal motivation and initiative to learn and (4) the culture of the units where nurses worked. CONCLUSION: The perpetual novice phenomenon exists due to a combination of both personal factors as well as contextual factors in the work environment. RELEVANCE TO CLINICAL PRACTICE: The results assist in directing future educational interventions and provide nursing leaders with the information necessary to create work environments that best enable practicing nurses to acquire and maintain clinical competence.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".