SS34-02 WORKPLACE HEALTH WITHOUT BORDERS’ VIRTUAL OCCUPATIONAL HEALTH AND SAFETY TRAINING USING SYNCHRONOUS AND ASYNCHRONOUS METHODS
Bibliographic record
Abstract
Abstract Introduction Training is a focus area of Workplace Health Without Borders (WHWB). There are 3,4 billion workers in the world. Nearly two-thirds of them work in unhealthy and unsafe conditions. Worldwide, there are about 8,000 certified / registered occupational hygienists and only 16 countries with professional accreditation programs. The global need is great for more trained occupational health / hygiene professionals who understand how to protect workers from workplace injury and disease. Materials and Methods This presentation will share our methods and platform for instruction. We will discuss how we pivoted during the COVID-19 pandemic to a combination of synchronous / asynchronous training, and how this solved several important issues for our students and tutors and overcame other barriers to in-person training. We will present the outcomes of student evaluations and how we measure and deliver successful training. Results The impact of our training will be presented. Our training leads to mentoring which leads to networking for the students and global professionals responsible for worker wellbeing. We engage in-country tutors for instruction and facilitation so that networking and mentoring are more easily attained. Conclusions Students and professionals in low- or middle-income countries (LMICs) lack access to occupational health / hygiene training. There is often a lack of regulations in these countries, so that prevention of exposure and protection of worker health is not addressed. However, knowledge of breaking the routes of exposure that cause disease can be used anywhere when it is understood.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.012 |
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".