SIMULATION: COMPANION CLINICAL PRACTICE FOR BACHELOR OF NURSING STUDENT LEARNING IN RESIDENTIAL CARE
Bibliographic record
Abstract
Nursing students learn in a variety of clinical settings including residential care. Faculties of Nursing administrators and educators understand the need for face to face student learning with older adults, and recognize potential consequences such as development or reinforcement of negative perceptions related to older adults. One strategy we intend to implement in the second year of the Bachelor of Nursing program at the Faculty of Nursing University of Calgary Canada is the use of simulation to improve student learning and attitudes toward older adults. We conducted a literature review to introduce simulation as a companion learning strategy to face to face practice in residential care settings. The purpose of the review was to identify what is known regarding the use of simulation to counter ageism that may be held by nursing students regarding older adults living in residential care. Our CINAHL, Medline (Ovid), and Google Scholar search using key words: ‘Nursing Education’, ‘Simulation’, and ‘Older Adult’ produced 26 articles. Five of the 26 met our inclusion criteria. Our analysis revealed: 1) undergraduate nursing students regard clinical simulation as a positive way to enhance their clinical experiences with older adults; and 2) due to limited evidence more research is required to understand if using simulation will improve student perceptions of older adults when learning and working in residential care. The presentation will outline findings from the literature review and include recommendations related to use of simulation in bachelor of nursing programs with a specific focus on caring for older adults.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".