Assessing the Feasibility of Using a Multi-Modal Simulation Approach to Prepare Nurse Practitioners in Primary Health Care
Bibliographic record
Abstract
Simulation based learning in nursing education provides learners with opportunities to practice real-life experiences.Enhancing the education of nurse practitioners (NPs) with simulation based teaching and learning strategies has not been well investigated.There is limited evidence related to learning outcomes and the use of high fidelity simulation or standardized patients.In an Ontario Primary Health Care Nurse Practitioner (PHCNP) Program, the use of a multi-model simulation learning activity was piloted with a group of NP learners.The learning activity consisted of three scenarios, each representing typical conditions seen in primary health care across the lifespan.Each scenario was carefully developed with consideration of curriculum goals, use of simulation technology or standardized patients, and the role of faculty facilitators.Learners worked in pairs as a team to complete a focused history and physical examination, formulate a diagnosis, and develop a plan of care or action for the patients.Following each of the three scenarios, the learner teams received focused feedback on their performance.A guided group reflection was conducted following the learning activity.The feedback from the learners was positive, with a recommendation to include similar learning opportunities earlier in the NP curriculum.The learners valued the active learning process, including peer collaboration and group debriefing.Although the findings from this pilot included a small group of learners, there are valuable considerations for nursing faculty teaching in NP programs with a primary health care focus.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".