What Constitutes an Expert Registered Nurse in Labour & Delivery?: A Phenomenological Inquiry
Bibliographic record
Abstract
The purpose of this study was to explore what constitutes an expert registered nurse in a labour and delivery unit. A qualitative, phenomenological approach was used to guide and analyze the interviews of twelve participants recruited through purposeful sampling. Patricia Benner’s From Novice to Expert theory was used as both a theoretical definition of expert as well as a baseline for participants to self-identify with one of the levels of skill acquisition (novice, advanced beginner, competent, proficient or expert). Three themes emerged from data analysis including: 1) characteristics of expert nurses, 2) significance and impact of loss and 3) difficulty with the word “expert”. The study results showed that expert is a fluid concept that is both difficult to define and maintain throughout a nurse’s career. Factors such as education, technology, culture, environment and most notably autonomy, impact a nurse’s ability to achieve expert status as well as the ability to remain an expert of the same capacity throughout their careers. In addition, environmental and practice related changes resulted in feelings of loss that also significantly impacted the nurse’s perception of expert nursing. Ultimately, it was identified that Benner’s definition of expert is not complete and would require additional research with a focus on relational and psychosocial elements of nursing specifically in the area of labour and delivery setting in order to achieve a more comprehensive definition.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".