Auditing the cost effectiveness of radon mitigation in the workplace
Bibliographic record
Abstract
Radon is a natural gas which can build up underneath buildings. The International Agency for Research on Cancer has found sufficient evidence to classify radon as harmful to human beings. The National Radiological Protection Board has identified areas in the United Kingdom where radon levels are above average. Northamptonshire is one such area, where the NHS was required to set up a radon mitigation programme to reduce the potential health hazard to its 11,189 employees, employed on 82 separate sites. Calculates the dose saving achieved and the associated costs and attempts to derive a value for the cost‐effectiveness of the programme, as compared to a programme recommended by the NRPB to reduce patient doses from dental X‐Rays in the UK. It also examines recent domestic remediation initiatives investigated by researchers in Spain, USA, Sweden and Canada. The methods used by Colgan and Gutiérrez to measure reductions in radon levels and to calculate associated annualised costs were used to analyse the results of the Northamptonshire NHS programme which produced an estimated cost of £680,000 per lung cancer saved. This paper reports on the costs and potential benefits delivered by the radon mitigation programme in Northamptonshire. It also discusses some of the wider policy implications for management, particularly in multi‐site public sector organisations where value for money in an environment of cash limited funding is an increasing pressure.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".