Evaluation of Personnel Radiation Monitoring in Radiodiagnostic Centres in South Eastern Nigeria
Bibliographic record
Abstract
To assess the level of personnel radiation monitoring in radio-diagnostic centres in South Eastern Nigeria. A cross sectional prospective survey that targeted radiographers working in ten selected government- owned hospitals in South Eastern Nigeria was conducted. The data collection instrument was a sixteen-item semi-structured self-completion questionnaire. Personnel radiation monitoring was available in only 4 out of 10 hospitals (40%) and in two of the hospitals radiation monitoring does not cover all the radiographers on employment. Radiation monitors were found to be read fairly regularly at about every quarter of the year but it takes more than 3 years for fresh supplies of radiation monitoring devices to be made in the hospitals where radiation monitoring is carried out. Radiation protection advisers or supervisors were available in only 4 hospitals (40%). Majority of the radiographers (41.5%; n = 17) believe the hospital management do not make provision for it in their budget. Dosimetric records of staff are not given any consideration during recruitment of new staff. Personnel radiation monitoring in South Eastern Nigeria is abysmally poor. This is a significant precautionary lapse as radiations risks cannot be assessed and corrective measures taken.
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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 | 0.002 | 0.005 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".