249a - Successful prevention strategies to prevent infectious airborne disease transmission in workers and our communities: a panel discussion
Bibliographic record
Abstract
Abstract Introduction & Moderation of the Panel: COVID-19 is a preventable disease, primarily caused by the SARS-CoV-2 virus aerosols entering the respiratory system. There is no dose-response, no exposure limits, and no simple instrumental methods for detecting the virus. Since the virus travels with infected people, a primary challenge has been developing hazard/risk assessments and safe work procedures to prevent the virus from entering and exiting workplaces. Four senior Occupational, Environmental, Health, and Safety (OEHS) professionals will present successful prevention initiatives that lowered the risk of occupational transmission of SARS-CoV-2. These initiatives encompass the development and content of Canada’s new respiratory protection standards; quantitative respiratory protection fit testing for South African health care workers; COVID-19 as a preventable occupational disease in the Canadian film industry. The panel is moderated by the CEO of the AIHA, an IOHA member-association, who will discuss making representations for worker and community prevention to national and state government agencies, as well as allied professional associations.
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 | 0.033 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.037 | 0.030 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".