A mixed-methods assessment of community-engaged learning in a Master of Public Health program
Bibliographic record
Abstract
Objective: Community-engaged learning is used in Master of Public Health programs to enhance student training, connect with communities, help solve societal issues, develop competencies, and build partnerships. However, it is unclear how much community-engaged learning components supplement existing Master of Public Health programs and prepare students in developing these competencies. Thus, the aim of this study was to apply an explanatory mixed-methods study design to evaluate a Canadian Master of Public Health program's community-engaged learning activities and propose recommendations to strengthen public health training and course delivery. Methods: = 11). Results: Community-engagement enhanced learning among Master of Public Health students, with the practicum placement, and program development capstone resulting in the largest self-reported development. Students in the focus group indicated community engagement provided skill and professional development, but also identified wanting additional curriculum coverage on various statistical software and qualitative research methods. Interviews with community partners revealed benefits of practicum placements such as mutual knowledge transfer, increased organizational capacity, and strengthened academic-community partnerships. Community partners also commented on challenges with recruitment, training, and aligning student-organization goals. Conclusion: The findings from this study suggest that an update to the Master of Public Health program curriculum, its core competencies, a combination of community-engagement activities, and future evaluations will be needed to advance education delivery.
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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 | 0.120 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".