Addressing the Unique Challenges of a Statewide Nurse Transition to Practice Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it