Prioritization of Occupational Sentinel Health Events for Workplace Health and Hazard Surveillance: The Pan American Health Organization Experience
Bibliographic record
Abstract
Within the ongoing strategy of the Workers' Health program at the Pan American Health Organization on strengthening surveillance in the field of workers' health in the Americas (the Region), a project was conducted in July 1999 in Washington, D.C. The objectives of the project were to discuss the problems found in the surveillance of workers' health, to develop a methodology and use it to prioritize and select the top three occupational sentinel health events for incorporation into the surveillance systems in the Region, and to develop the initial protocols for establishing the surveillance systems for the three chosen occupational sentinel health events. The Pan American Health Organization invited 24 occupational health experts from the Region to participate in the project. Over an intensive 3-day period, the experts heard reports of workplace surveillance activities in the various countries in the Region; developed prioritization methods; identified and prioritized a list of occupational sentinel health events; and developed three surveillance protocols, one each for occupational fatal injuries, pesticide poisoning, and low back pain. The results on the prioritization of occupational sentinel health events and the development of surveillance protocols are reported in this article.
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 | 0.101 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".