124 Functional requirements for a new risk assessment tool: the first step - a new inhalation model to assess exposure to bioaerosols
Bibliographic record
Abstract
Abstract Within Occupational Hygiene, risk assessments due to exposure to biological agents has received less emphasis compared to risks associated with other hazardous substances. Thus, the tools for hazard inventory, risk assessment, and implementation of control measures for biological agents remain scarce. The COVID-19 pandemic raised awareness on the magnitude of potential health consequences linked to exposure to biological agents also in workplace environments. But concerns should not be limited to infectious biological agents. Non-infectious microorganisms might also impact in workers’ health due to their sensitizing, toxic and even carcinogenic effects. Workers in various sectors may be exposed through aerosols or contact with infected persons or materials that are contaminated with microorganisms. Different qualitative risk assessment tools for biological agents have been developed over the last decades, with differences in their scope, parameters used and results obtained. In order to propose a functional design for the development of a new risk assessment tool related to biological agents, four existing tools were compared to better understand their strengths, limitations and applicability. Based on this comparison, a general structure for a complete new tool was proposed with some key requirements for determining occupational exposure to biological agents. Furthermore, we developed a new qualitative risk assessment model for bioaerosol inhalation created from a source-receptor conceptual model where scores for each parameter were assigned based on literature. The current model has been applied to seven different sectors and tested in more than 120 real workplace scenarios with satisfactory results.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".