Development and Evaluation of a New Security and Privacy Track in a Health Informatics Graduate Program: Multidisciplinary Collaboration in Education
Bibliographic record
Abstract
BACKGROUND: The widespread application of technologies such as electronic health record systems, mobile health apps, and telemedicine platforms, has made it easy for health care providers to collect relevant data and deliver health care regimens. While efficacious, these new technologies also pose serious security and privacy challenges. OBJECTIVE: The training program described here aims at preparing well-informed health information security and privacy professionals with enhanced course materials and various approaches. METHODS: A new educational track has been built within a health informatics graduate program. Several existing graduate courses have been enhanced with new security and privacy modules. New labs and seminars have been created, and students are being encouraged to participate in research projects and obtain real-world experience from industry partners. Students in this track receive both theoretical education and hands-on practice. Evaluations have been performed on this new track by conducting multiple surveys on a sample of students. RESULTS: We have succeeded in creating a new security track and developing a pertinent curriculum. The newly created security materials have been implemented in multiple courses. Our evaluation indicated that students (N=72) believed that receiving security and privacy training was important for health professionals, the provided security contents were interesting, and having the enhanced security and privacy training in this program was beneficial for their future career. CONCLUSIONS: The security and privacy education for health information professionals in this new security track has been significantly enhanced.
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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 | 0.035 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".