Measuring Health Promoter Core Competencies Among Graduate Students Enrolled in a Health Promotion Course
Bibliographic record
Abstract
Global public health events such as the COVID-19 pandemic create public awareness of the need for skilled public health graduates and public health graduate programs. Core Competencies for Public Health are tools that can guide graduate-level public health programs to ensure students are receiving the appropriate knowledge, tools and skills required to become effective public health practitioners that comprise a strong public health workforce. Case-based learning is a learner centered approach that allows students to apply their knowledge to real-world scenarios, promoting higher-order thinking. The University of Guelph Master of Public Health graduate program incorporates both the Core Competencies for Public Health in Canada as created by the Public Health Agency of Canada and the Pan-Canadian Health Promoter Competencies developed by Health Promotion Canada (HPC), as well as experiential learning to ensure students are receiving a high-quality learning experience throughout the course of their studies. Assessments of the program, individual courses within the program, and assignments within the courses are beneficial to ensure alignment with the Public Health Core Competencies—including Health Promotion Core Competencies. In the present study, surveys were administered to students enrolled in the University of Guelph Winter 2021 semester Health Promotion class before and after completion of an experiential group health promotion assignment worth 50% of their grade. The assignment used case-based learning within a group setting, allowing students to engage and apply knowledge gained through the semester to solve a real-world public health issue. Students reported increased self-perceived proficiency in all Core Competencies after completion of the practice-based group health promotion program assessment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".