Design, Development, and Content Creation for an Open Education Physics Website for MRT Education
Bibliographic record
Abstract
BACKGROUND: As health care technologies continue to advance rapidly, resulting in improved standards of practice, it is essential for health care professionals to continually expand on their current skills and knowledge. We describe here an initiative to use open education resources to provide ongoing education in radiation medical sciences and imaging. AIMS: The aim of this study to design an interactive, engaging, multilevel radiation medical physics resource, which is fully open to the public, and functional on all types of computing devices. Our primary target audiences are students and workers in medical radiation technology and other health care professionals as part of their continuing professional development. DESIGN AND DEVELOPMENT: The three tasks of design, development, and content creation were most efficiently performed in parallel wherever possible. A modern responsive web design was adopted to target all desktop and mobile devices. Only open-source tools and libraries were used in developing the OpenPhys website. OVERALL WEBSITE DESIGN AND NAVIGATION: The homepage is a modern tile-based design containing one coloured tile for each lesson. Clicking anywhere on a coloured lesson tile will open up a two-dimensional interactive concept map linking to content pages. Currently, 10 lessons are available online ranging from the electronic structure of the atom to MRI basics: "NMR" and "Inside a Pixel". Lesson pages include text, images, graphics, equations, quizzes, and interactive animations. USER FEEDBACK: An online questionnaire was emailed to current radiation therapy students at the University of Alberta and alumni regarding the functionality and navigation of the website. DISCUSSION/CONCLUSION: To our knowledge, OpenPhys is the first open education resource specializing in radiation physics and medical imaging. We believe OpenPhys will fill existing gaps in the realm of physics education delivery and could be a component of a blended learning initiative. Future steps will include a formal evaluation of the website and content.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".