Bibliographic record
Abstract
Technically oriented industries demand job-ready skill sets from employees upon the immediate completion of their post-secondary studies. To meet these needs, many post-secondary institutions have mandated the incorporation of simulation-based learning (SBL) into curriculum, across a wide array of disciplines (Fang, Tan, Thwin, Tan, & Koh, 2011). The purpose of this research was to explore the experiences of three recent graduates of a paramedic program that had engaged in an ambulance simulator used in curriculum at a western Canadian post-secondary institution. An investigation examined how the design and associated physical interactions within an industrial simulation, facilitated in this post-secondary institution, affected learning outcomes and emotion responses of the research participants. Interview data gathered revealed differing personal experiences grouped into four categories associated with learning in SBL: realism, facilitation, learning outcomes, and personal responses. For SBL to be compelling to the learner, it must be realistic, facilitated by properly trained staff, and aligned with clearly established and valid learning outcomes capable of inducing physical and emotions responses. The incorporation of an ambulance simulator to augment a program already rich in SBL was an effective training tool for use in this mobile-healthcare application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".