Collaboration is key to success for transition of newly licensed nurses to specialty areas
Bibliographic record
Abstract
Newly licensed nurses gain knowledge and skills at the academic level to enter the profession as novice nurses. A nurse residency program is crucial in the successful transition of new nurses to their professional role. In addition, supportive structures are essential for new nurses to acquire the skills, knowledge, and decision-making abilities appropriate for their specific area of practice. At Houston Methodist, an additional element of the nurse residency program includes transition to practice classes that are designed to increase new nurses’ knowledge and understanding of relevant skills. The classes provide practice in specific environments and improve self-confidence with elements identified through Casey-Fink surveys. In addition to the initial classes developed to support these areas, feedback showed the need to incorporate specific classes for specialized environments. As a result, the coordinators of the nurse residency program, experts, and leaders from specialty areas explored and developed specific learning opportunities. The aim of this article is to showcase the strategies used to develop customized approaches to ensure successful transitions to practice for newly licensed nurses.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".