Abstract P-094: ADVANCE PRACTICE NURSE (APN) ORIENTATION: CREATING OPPORTUNITIES FOR NOVICE APN DEVELOPMENT
Bibliographic record
Abstract
Aims & Objectives: Limited published literature suggests that structured and phased orientation best supports role development of novice advanced practice nurses (APN). We describe an alternate approach to supporting our transition from novice to proficient APN in a critical care environment, as described by Benner’s stages of clinical competence. Methods Through the application of ongoing reflective practice, iterative planning, and customized composite mentorship, the novice APN’s devised creative strategies for consolidating advanced clinical skills, developing project leadership abilities, establishing scope and focus for advanced practice and gaining scholarly proficiency were constructed. Results This unique approach to APN development was successful and we have efficaciously achieved our transition to proficient APNs. Successes with building a supportive APN community of practice, and the resulting mentoring and learning partnerships will be described and can inform APNs and nurse leaders about the challenges and benefits of self- directed orientation strategies. Conclusions The transition from novice to expert APN is complex and is compounded by the intensity of critical care practice. Although a structured and phased orientation is thought to be ideal, successful transition can be achieved when motivated clinicians apply these unique strategies for APN development.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".