Radiation Safety Awareness Amongst Medical Students
Bibliographic record
Abstract
ABSTRACT Objectives: To assess awareness of radiation safety amongst medical and dental students and determine how their knowledge could be improved. Methods: An anonymous electronic survey of medical and dental students at a Canadian university was conducted between February and April 2016. It was made up of 15 close-ended questions assessing knowledge and practice of radiation safety measures. Participants were also questioned about their willingness to learn more about radiation safety measures and the type of educational intervention. Results: They were 87 responses, of which 39.1% (n=34) were males and 60.9 % (n=53) were female. Most students (83%, n=39) indicated that they had never practiced radiation safety measures. While most students (81.7%, n=67) had an idea or good idea about radiation safety measures, 45.4% (n=25) had never used them. Most students (77.1%, n=64) wanted to learn more about radiation safety in the form of workshops, seminars, and online modules. Students were divided as to when radiation safety education was appropriate. While 47.6% (n=40) thought that it would be most appropriate to have them during undergraduate degree, 48.8% (n=41) thought that it would be more appropriate during clinical rotations. Most students (75%, n=63) had never completed an educational course on radiation safety and only 9.5% (n=8) knew the annual permissible occupational radiation dose. Most students (76.9%, n=30) did not track their annual occupational radiation exposures. Conclusions: Radiation safety awareness among medical and dental students needs to be improved. Students are willing to learn and improve their knowledge about radiation safety awareness especially during clinical training or college/undergraduate studies
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".