Iteration and Communication During the Development of a Smartphone Endoscope Adapter
Bibliographic record
Abstract
A smartphone endoscope adapter was developed by students for Health PEI to reduce the impact of the limited access to healthcare in rural communities. The adapter allows doctors to connect their smartphones to endoscopes to record procedures, which can aid practitioners in remote diagnosis, record keeping, and follow-up appointments. This article contains a case study of the eight-month service-learning experience to offer insights from two students and their instructor in the second-year engineering design course. The design process, communication, and iteration are outlined in the case study. Throughout the experience, twelve CAD models and seven physical prototypes were developed, and the students continued to iterate and test the device after the academic year ended, demonstrating their devotion to the project. The involvement of the community partner influenced the quality of the design, the motivation of students, the timeline, and the number of iterations. Also, the students’ desire to positively impact a community by providing access to technology was a motivating factor throughout the project, which aligns with the intentions of a service-learning project. A discussion on the impact of reflection and iteration is offered and recommendations are provided for instructors, students, and community partners to optimize service-learning experiences.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".