Addressing the Unique Challenges of a Statewide Nurse Transition to Practice Program
Bibliographic record
Abstract
The post-pandemic healthcare landscape significantly impacted the professional nursing workforce by exacerbating existing challenges, including the academic-practice gap of new nurse graduates. Transition to practice (TTP) programs have been proven effective in supporting newly licensed registered nurses as they move into practice. A well-designed TTP program empowers new nurses to become resilient and competent, enhancing patient care and contributing to a healthier work environment. While these programs have been instituted throughout the country, most are in acute care settings, primarily in urban areas. The authors present a model for creating a transition to practice program designed to address the unique challenges faced in rural areas. The step-by-step process the Arizona Hospital and Healthcare Association (AzHHA) used to set up a statewide transition to practice program geared towards small and rural facilities and those serving the underserved is presented. The critical partnership with OpusVi, who was contracted for a customized curriculum to address the unique needs of hospitals, such as critical access and behavioral health is outlined. Finally, concrete actions that can be taken and a roadmap for program assessment are offered.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".