Using core competencies to build an evaluative framework: outcome assessment of the University of Guelph Master of Public Health program
Bibliographic record
Abstract
BACKGROUND: Master of Public Health programs have been developed across Canada in response to the need for graduate-level trained professionals to work in the public health sector. The University of Guelph recently conducted a five-year outcome assessment using the Core Competencies for Public Health in Canada as an evaluative framework to determine whether graduates are receiving adequate training, and identify areas for improvement. METHODS: A curriculum map of core courses and an online survey of University of Guelph Master of Public Health graduates comprised the outcome assessment. The curriculum map was constructed by evaluating course outlines, assignments, and content to determine the extent to which the Core Competencies were covered in each course. Quantitative survey results were characterized using descriptive statistics. Qualitative survey results were analyzed to identify common themes and patterns in open-ended responses. RESULTS: The University of Guelph Master of Public Health program provided a positive learning environment in which graduates gained proficiency across the Core Competencies through core and elective courses, meaningful practicums, and competent faculty. Practice-based learning environments, particularly in collaboration with public health organizations, were deemed to be beneficial to students' learning experiences. CONCLUSIONS: The Core Competencies and graduate surveys can be used to conduct a meaningful and informative outcome assessment. We encourage other Master of Public Health programs to conduct their own outcome assessments using a similar framework, and disseminate these results in order to identify best practices and strengthen the Canadian graduate public health education system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".