Bibliographic record
Abstract
Occupational health and safety (OH&S) is related to the health, safety, and welfare issues in the workplace. It is guided by laws, standards, and programs aimed at making the workplace safer for workers, along with co-workers, family members, customers, and other stakeholders. Improvement in a company's occupational health and safety standards has the potential to improve the overall business environment and contribute to a better quality of work life. Brazil has historically reported high numbers of work accidents, which may have serious consequences to workers, resulting in permanent disability or even death. The country has also been developing some very successful policies related to worker health during the last years. The objectives of this paper were to analyze the evolution of statistics of work accidents in Brazil and the impact of some demographic and work variables in these numbers. Although there is a high incidence of workplace accidents in Brazil, there has been a reduction in the incidence of accidents, death, and accident-driven retirement. Despite the identified progress the situation is still extremely worrying. Responsibility for the construction, promotion and maintenance of a safe work environment should be shared by everybody - organizations, workers, unions, health system, among others, as being safe at work is a central aspect for quality of life.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".