Implementation of a tiered, competency based orientation program
Bibliographic record
Abstract
Background and objective: Orientation programs for new graduate nurses in acute-care hospital settings aim to guide the transition from student to practicing nurse. Typically, these programs incrementally increase the workload of the new graduate nurse while providing clinical guidance. While effective, the model is time consuming and costly. The objective of this educational study was to ‘flip’ the model and determine the benefit of new graduate nurses initially providing specific care that aligned with their clinical skills and increased their clinical responsibilities as their skill set expanded. Clinical progression and orientation satisfaction scores were used to determine the program’s success.Methods: Guided by experiential learning theory and the skills acquisition model, competency was assessed by mastery. Thus, rather than exposing new graduate nurses to a single patient and moving toward providing care to an expected workload, orientation was holistic in nature and focused on the acquisition of clinical skills, from simple to complex.Results: Data from the seven participants reveal that none required additional orientation time or supplemental instruction. All seven new graduate nurses remained employed on the units of their orientation and successfully transitioned into professional nursing roles.Conclusions: Outcomes from this study included increased new graduate retention and a decrease in the time required to achieve clinical competency. Both outcomes resulted in a financial benefit to the acute-care facility. New graduate nurse and preceptor satisfaction with the study demonstrate the ability to mitigate the stress and anxiety associated with transiting to clinical practice.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".