Occupational Radiation Exposure in Health Care Facilities
Bibliographic record
Abstract
BACKGROUND: Radiation, which is used extensively to diagnose and treat human diseases, poses an occupational health risk for the concerned health workers. Personal dosimetry is an important tool to monitor occupational radiation exposures. OBJECTIVE: This study was conducted to reveal and to describe the situation of occupational radiation exposure monitoring among staffs in different health care facilities in Nepal. METHODS: A cross-sectional study was performed among the 35 Health Care Facilities. Information about types and number of X-ray procedures performed, types and number of personnel involved, workload and the availability of personal dosimetry service were collected. RESULTS: Six Health Care Facilities had personal dosimetry service available for a total of 149 personnel. Of a total of nearly one million X-ray procedures performed in the 35 Health Care Facilities in 2007, 76 percent was performed by non-monitored personnel. The majority of the facilities performing high dose procedures, like catheterisation, angiography and intestinal barium procedures did not offer personal dosimetry for the involved personnel. CONCLUSION: There are a limited number of personnel being monitored with personal dosimetry. There are no regulatory dose limits for occupationally exposed staff. Thus, there is an urgent need to establish a national radiation protection authority to regulate the use of radiation in Nepal.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".