The Houston Methodist nurse residency program journey: Transitioning the new graduate nurse into a success
Bibliographic record
Abstract
The transition of new graduate nurses (GNs) to professional practice has its challenges, thus providing an established program to facilitate this journey can lessen some of these challenges. Various approaches exist to help GNs transition into their practice environment. This article describes the Houston Methodist Nurse Residency Program (HMNRP), a successful transition program for GNs within a mutli-facility health care system. Houston Methodist (HM) moved from independent practices to a unified system approach to provide a combination of centralized and decentralized nurse residency program sessions to meet the needs of the GNs. This innovative approach has ensured the success of the program. Multiple strategies are important for an effective systematic approach. Some of these strategies include (1) identifying the ideal players, including coordinators, facilitators, stakeholders, and content experts and (2) providing the resources needed to achieve the desired results. Establishing a unified approach to ensure that outcomes are met is essential to success. Defining goals and desired outcomes will guarantee that the purpose of the program is achieved. A multi-faceted approach can be used to teach and facilitate the sessions, continuous assessment and program evaluation help to identify opportunities for improvement. Including all key stakeholders in the evaluation and future planning allows for the program to evolve to meet the outcomes and needs of all involved. Planning is a vital component to ensure a smooth transition. Program success truly lies in the planning.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".