Home health simulation: Helping students meet the changing healthcare needs
Bibliographic record
Abstract
Background/Objective: Nursing education has traditionally educated students in an acute care setting. However, recent trends in health care delivery models have moved the care of clients to a variety of out-patient settings. For the role of the nurse educator to transform, the curriculum must be able to expand beyond just the hospital-based focus to also include a community-based focus. To meet this demand, the College of Nursing faculty created a geriatric home care simulation to enhance the students’ experiences of providing care beyond the hospital setting to this growing population.Methods: A gap analysis of current simulations imbedded within the curricula identified the need for a community-based simulation and a geriatric home care simulation was chosen. The American Association of Colleges of Nursing Essentials of Baccalaureate Education and Recommended Baccalaureate Competencies and Curricular Guidelines for the Nursing Care of Older Adults were used as the framework for the analysis.Results: This simulation was designed as an interactive, low-stakes experience since many students lacked the opportunity to experience this unique clinical care setting. This article describes the development of the simulation, specific content objectives and outcomes, summary of the reactions of the students and faculty, as well as revisions and closing reactions.Conclusions: Nursing education has historically focused on acute care and now nursing students must be able to care for clients in a variety of out-patient settings, while focusing on the management of chronic diseases, promoting wellness and disease prevention. The future of nursing education will continue to require that faculty members explore innovative solutions to meet the educational needs of students, while balancing the health care needs of various populations and our changing health care delivery systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".