Adding Value to Orientation Through the Use of Skills and Simulation-Based Learning.
Bibliographic record
Abstract
Workshop Topic: Clinical Placement/Orientation The use of primarily didactic methods of education can lead to learner disengagement, lack of knowledge retention and dissatisfaction (Culley et al, 2012). Feedback from previous surgery nursing orientations at the Ottawa Hospital has consistently shown that there was a need for a more engaging and hands on orientation. Adult learners are more engaged when learning is relevant and applicable to practice. Educators must recognize the life and practice experience of the adult learner (Billings and Halstead, 2009). Comprehensive orientation should be hands on, engaging, interactive, and evidenced based, incorporating institutional polices and best practices (Lamers et al, 2013). Taking feedback into consideration, the orientation program was improved with support from managers, directors and other key stakeholders. Through the use of low to medium fidelity skills, and high fidelity simulation, the nurse educators were able to revise the orientation from a primarily didactic model to a skills and simulation based program that is more applicable to practice. Taking the fiscal climate into consideration, this program is cost neutral, but has added great value in learning outcomes and indirectly patient safety. In this workshop participants will have the opportunity to develop and discuss the value of utilizing simulation and skills into an orientation program for new staff within the context of their current onboarding processes. Upon completion of this workshop the participant will: • Reflect upon one's current orientation program structure • Consider adding value to orientation by incorporating skills and simulation • Identify key stakeholders within one's own organization related to orientation program development • Develop a framework for orientation utilizing simulation and skills • Gain knowledge related to making changes to the orientation process within one's organization Open Access Abstract
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".