Using Simulation-Based Methods to Support Demonstration of Competencies Required by Micro-Credential Courses
Bibliographic record
Abstract
The rise of the digital revolution has disrupted entire industries and job markets, leading individuals to either upgrade or transfer their skills in order to continue within their designated fields or transition to new workplace contexts. Employers expect their employees to apply their knowledge to real-world settings, analyze and solve problems, connect choices to actions, and innovate and create. Moreover, the COVID-19 pandemic has exacerbated changes to the educational landscape by forcing online and remote contexts; physical distancing and other preventive measures have necessitated a shift towards increasing the use of disruptive digital technologies- extended reality (e.g., virtual and augmented reality), gaming, and additive manufacturing-in simulation delivery. Yet Canada's economic and demographic data suggests that many new graduates struggle to transition from school to working life. The confluence of these factors has led to a need for both individuals and higher education institutions to upgrade and adapt to new digital techniques and modalities. As these needs grow, simulation-based education (SBE) techniques and technologies-already an integral part of training for some professions, including nursing, medicine, and various other health professions-are increasingly being used in digital contexts. In this editorial, we provide our perspective of the socio-technological movement associated with health-professions education (HPE) within the SBE context and examine the application and implementation of micro-credentialing within this field. We also discuss the various levels of expertise that learners may acquire. From this vantage point, we address how SBE can complement the assessment of competencies that learners must demonstrate to attain micro-credentials and explore micro-credentialing's advantages for, and use in, HPE.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".