Preparing the occupational safety and health workforce for future disruptions
Bibliographic record
Abstract
BACKGROUND: Despite some emerging lessons learned from the COVID-19 pandemic, evidence suggests the world remains largely underprepared for-and vulnerable to-similar threats in the future. METHODS: In 2022, researchers at the US National Institute for Occupational Safety and Health (NIOSH) led a team of volunteers to explore how future disruptions, such as pandemics, might impact work and the practice of occupational safety and health (OSH). This qualitative inquiry was framed as a strategic foresight project and included a series of activities designed to help better understand, prepare for, and influence the future. RESULTS: Findings from a thorough search for indicators of change were synthesized into nine critical uncertainties and four plausible future scenarios. Analysis of these outputs elucidated three key challenges that may impact OSH research, policy, and practice during future disruptions: (1) data access, (2) direct-to-worker communications, and (3) mis- and dis-information management. CONCLUSIONS: A robust strategic response is offered to address these challenges, and next steps are proposed to enhance OSH preparedness and institutionalize strategic foresight across the OSH community.
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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".