Progressive care orientation: Incorporating a program that supports transition to practice
Bibliographic record
Abstract
Objective: Progressive care units (PCUs), also sometimes called intermediate care units, improve the utilization of beds within intensive care units while adjusting the workload of the nurse. PCUs encompass a scope of care between the critically ill patient and the acute care patient. Owing to the advanced skills set needed, nurses with limited experience in this setting may benefit from an orientation course in addition to on-the-job training. The purpose of this project was to develop and evaluate an orientation program for nurses working in progressive care settings at a multi-site hospital system.Methods: Kolb’s experiential learning theory and adult learning theory were used as a framework to plan and design a 2-day instructional program that addressed the cognitive, psychomotor, and affective aspects of learning needs.Results: A total of 244 participants completed the 2-day program. The teaching strategies were shown to be effective, as indicated by survey results reporting a mean score of 4.36 on a 1-5 Likert scale (with a score of 1 indicating the presenter did not clearly articulate the subject and 5 indicating that the presenter clearly articulated the subject). The participants stated that they intended to make changes in practice and identified changes to improve the program (e.g., inclusion of high-fidelity manikins, patient-controlled anesthesia).Conclusions: Incorporating a specialized training program for newly licensed nurses and nurses transitioning to the PCU with less than 2 years’ experience in this setting may improve the nurse’s confidence and performance of patient care skills in this highly acute environment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".