People do not have high levels of knowledge of low dose ionizing radiation (LDIR)
Bibliographic record
Abstract
Through survey and focus groups in two provinces in Canada misunderstanding and confusion surrounding Low-Dose Ionizing Radiation (LDIR) is explored specifically surrounding medical procedures, risk, and benefits. Generally people associated the word radiation with harm, but when asked participants were not concerned about LDIR. Approximately equal numbers (40%) thought LDIR was 'difficult' as those that thought it was 'easy' but research results reveal confusion about the definition of and sources of LDIR. Most people believed the benefits of LDIR outweighed the risks. Further, many had inaccurate views of 'high' dose radiation. Scientists and the Canadian regulator were determined to be the most trusted sources of information while elected officials and industry representatives the least trusted. Participants wanted more information on whether LDIR was a problem in Canada, what the risks were associated with it, as well as the applicable protections, rules and regulations. Focus group participants expressed a preference for face-to-face exchange of information, but mass media remains an important source of information as the first-place people check for answers. Future research surrounding behavioural science and LDIR communications, and deep LDIR science communication will be important in addressing this issue.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".