Partnering with Students to Develop a Capstone for a Graduate Health Informatics Program
Bibliographic record
Abstract
OBJECTIVE: This study aimed to assess the desirability, feasibility, and sustainability of integrating a project-based capstone course with the course-based curriculum of an interdisciplinary MSc Health Informatics program guided by a student-partnered steering committee and student-centered approach. METHODS: = 18) of health informatics students and alumni. Survey data were analyzed descriptively. Focus groups were audio-recorded and transcribed verbatim and then analyzed using a general inductive and classic analysis approach. RESULTS: Most students supported including a capstone project but desired an option to work independently or within a group. Students perceived several benefits to capstone courses while concerned over perceived challenges to capstone implementation, evaluation, and managing group processes. The themes identified were (1) professional development, identity, and career advancement, (2) emulating the real world and learning beyond the classroom, (3) embracing new, full-circle learning, (4) anticipated course structure, delivery, and preparation, (5) balancing student choice, interests, and priorities, and (6) concerns over group dynamics, limitations, and support. CONCLUSION: This study demonstrates the value of having students as partners at each stage in the process from methods conception to course curriculum design. With the steering committee and the curriculum developer, we codeveloped a student-centered course that integrates foundational digital health-related project knowledge acquisition with an inquiry-based project that can be completed independently or in small groups. This study demonstrates the potential benefits and challenges that health informatics educators may consider when (re)designing capstone courses.
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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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".