The development of a mental health low-fidelity simulation to enhance undergraduate nursing student knowledge and competency
Bibliographic record
Abstract
Background: The purpose of this practicum project was to develop a low-fidelity mental health simulation to enhance knowledge and competency among Cape Breton University undergraduate nursing students. Low-fidelity simulation is an active learning strategy to enhance traditional teaching methods and introduce students to mental health concepts prior to clinical placement. Methods: A literature review was conducted to compare active learning techniques and undergraduate nursing student learning outcomes. An environmental scan was completed with undergraduate nurse educators and institutions in Atlantic Canada to identify current mental health teaching strategies and barriers to mental health learning. Consultations were completed with Cape Breton University nursing students and nursing faculty to identify perceived mental health learning needs. Results: Published literature showed that low-fidelity simulation benefits undergraduate nursing students by developing cognitive and affective knowledge; enhancing confidence; and improving overall satisfaction. The environmental scan helped identify that universities differ in their teaching strategies (e.g., mental health theoretical course versus a concept-based approach). Mental health simulation is not often utilized, and clinical placements vary in duration. Nurse educators identified there is limited mental health laboratory time with competing demands of the programs and limited funding for implementation. Consultees identified the laboratory as necessary to prepare students for mental health clinical and the use of low-fidelity simulation is a solution to funding challenges. Low-fidelity simulation was favored among students when compared to other mental health simulation techniques. A mental health low-fidelity simulation was developed for undergraduate nursing students at Cape Breton University. The simulation consists of student learning objectives, a case scenario, a detailed facilitative approach to delivery, preparation material, pre-brief, debrief, and methods for evaluating student learning outcomes. Conclusion: Low-fidelity simulation will provide students with the necessary practice and guidance to expand their knowledge of mental health concepts to enhance competency in engaging with clients seeking mental health treatment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".