An Innovative Approach to Health Sector Regulatory Compliance Education
Bibliographic record
Abstract
The essay presents the authors' experience implementing a competency-based education (CBE) pilot program for the Health Sector Regulatory Compliance (HSRC) graduate certificate program at Humber Polytechnic. It also explores the authors' experience of the design, execution, and outcomes of an innovative 18-credit integrated course, “Health Sector Regulatory Skills in Practice” (HSRC 5020), which consolidated learning outcomes from four second-semester courses. The CBE approach prioritizes skill mastery over traditional credit-hour models, addressing the growing skills gap between academia and industry. The HSRC program’s pilot focused on students demonstrating four core competencies: Audit and Inspection Management, Risk and Compliance Management, Regulatory Research and Analysis, and Trending and Data Analysis. Faculty evaluated each competency on a scale from ‘Foundational’ to ‘Developing’ to ‘Proficient,’ providing students with regular feedback and coaching sessions. The course followed a 7-1-7 format, combining structured learning periods with a mid-term break. Implementation involved course design considerations, resource allocation, and student engagement through weekly coaching sessions and project-based learning activities. Assessment methods were diverse and authentic, including written reports, oral presentations, and digital portfolios, allowing students to demonstrate their skills through differentiated formats. The student feedback highlighted the benefits of self-paced learning, practical application of skills, and course flexibility. Students valued the autonomy to control their educational journey, emphasizing real-world scenarios. However, the challenges noted include students’ inexperience with digital portfolios and the need for enhanced communication with instructors. [Abstract continued in the article PDF.]
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".