Operating room nurse residency and specialty educators: Paramount in the success of novice nurse retention
Bibliographic record
Abstract
Perioperative service is one of the specialties of nursing in which a team approach is vital for optimal patient care. The registered nurse is responsible for coordinating and delivering safe patient care. Operating room (OR) nurses are responsible for applying fundamental applications of the nursing process while formulating plans of care unique to surgical patients. The growing shortage of nurses worldwide especially impacts highly complex areas such as the OR, where skills specialized are needed to care for patients. One of the largest challenges of a graduate nurse (GN) is becoming enculturated to new environments. Traditionally, OR nursing is a paradigm foreign in nursing curricula; this creates challenges in the GN population in applying their practical nursing skills to surgical patients. In an effort to combat ongoing knowledge deficits unique to OR nursing, Houston Methodist Hospital (HMH) created an OR nurse residency program. The literature suggests that specialty-specific nursing residency programs offer GNs essential tools for becoming successful in their transition. Additionally, research suggests reductions in nurse burnout and turnover rate among GNs with adequate training and preparation. The purpose of this article was to provide insight on the importance of introduction to the OR prior to graduating from nursing school and the importance of OR nursing specialty residency programs and specialty educators as they pertain to the ideal nursing transition, sustainability, retention, and favorable patient outcomes. A questionnaire was created to capture successful applicable practices; the questionnaire also provided an opportunity for GNs to suggest opportunities for program improvements. The questionnaire was used to explore feedback from the summer 2014 Operating Room (OR) residency program graduate nurses in an effort to capture improvements needed for future program success.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".