Innovation in clinical learning: The AM/PM model
Bibliographic record
Abstract
Objective: A primary goal in clinical learning is to apply nursing knowledge and skills learned in the classroom to clinical. While benefits to learning in clinical are evident, this experience is not without challenges, which often relate to coordination of the learning experience. The AM/PM model, an innovative clinical learning model, was developed in response to scheduling challenges that impacted learning.Methods: A Bachelor of Science in Nursing program at a state university in the western region of the United States was using a 12-hour biweekly shift schedule for clinical rotations. This schedule negatively impacted learning. Thus, a 6-hour weekly (AM/PM) clinical learning model was developed and implemented to address barriers in clinical learning, using Lewin’s Theory of Change as the theoretical framework and as a guide to achieving the desired change. Standardized examination performance was used as a measure of success to evaluate summative learning.Results: Clinical learning was improved as a result of implementing the AM/PM model. Nursing students had more opportunity to develop critical thinking, clinical judgment, and communication skills. Learning outcomes measured by standardized exam scores increased for the AM/PM groups.Discussion and conclusions: The AM/PM model, in comparison to other traditional clinical models, was successful in providing experiences to support critical thinking, clinical judgment, and improved learning outcomes. Using Lewin’s Theory of Change as a theoretical framework to guide implementation of the AM/PM model supported all key stakeholders in adapting to the change, ultimately supporting nursing student learning.
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.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".