Spaced Education and the Importance of Raising Awareness of the Personal Data Protection Act: A Medical Student Population-Based Study
Bibliographic record
Abstract
BACKGROUND: The Personal Data Protection Act (PDPA) of Singapore was first passed in 2012, with subsequent enforcement regulations effective in 2014. Although medical education via digital platforms is not often used in medical schools in Singapore as of yet, many current means of communication at all levels in the medical community from medical schools to clinics to hospitals are unsecure and noncompliant with the PDPA. OBJECTIVE: This pilot study will assess the effectiveness of MyDoc, a secure, mobile telehealth application and messaging platform, as an educational tool, secure communications tool, and a tool to raise awareness of the PDPA. METHODS: By replacing current methods of communication with MyDoc and using weekly clinical case discussions in the form of unidentifiable clinical photos and questions and answers, we raised awareness the PDPA among medical students and gained feedback and determined user satisfaction with this innovative system via questionnaires handed to 240 medical students who experienced using MyDoc over a 6-week period. RESULTS: All 240 questionnaires were answered with very positive and promising results, including all 100 students who were not familiar with the PDPA prior to the study attributing their awareness of it to MyDoc. CONCLUSIONS: Potential uses of MyDoc in a medical school setting include PDPA-compliant student-to-student and student-to-doctor communication and clinical group case discussions with the sharing of patient-sensitive data, including clinical images and/or videos of hospital patients that students may benefit from viewing from an educational perspective. With our pilot study having excellent results in terms of acceptance and satisfaction from medical students and raising awareness of the PDPA, the integration of a secure, mobile digital health application and messaging platform is something all medical schools should consider, because our students of today are our doctors of tomorrow.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".