A Study on Causal Relationships Between Working Conditions and Occupational Diseases: A Case Study of a Phosphorus Plant in Kazakhstan
Bibliographic record
Abstract
This article examines the relationship between working conditions and the development of work-related diseases at a phosphorus plant.An analysis of occupational risks was conducted based on indicators of relative risk (RR) and etiologic fraction (EF), using data from medical examinations, temporary disability morbidity, and working conditions assessments.The study involved 1,162 workers, of whom 1,021 comprised the experimental group (employees from five workshops of the phosphorus plant exposed to harmful factors), and 141 formed the control group, with no exposure to hazardous occupational factors.By comparing the levels of morbidity, injury, and occupational pathology between these groups, significant differences were found in the levels of risk and the degree of professional conditioning of diseases.The analysis revealed that the highest levels of occupational risk were identified in Workshop No. 12 (RR=4.3;EF=76.7%) and Workshop No. 5 (RR=2.5;EF=60.64%),indicating a strong occupational attribution of the detected pathologies, primarily affecting the sensory and respiratory systems.A moderate level of risk was established in Workshop No. 7 (RR=1.6;EF=37.5%) and was predominantly associated with musculoskeletal disorders.In contrast, Workshops No. 2 (RR=1.1;EF=9.1%) and No. 1 (RR=0.7;EF=-42.9%)demonstrated low risk levels, suggesting a weak or negligible association between working conditions and workers' health outcomes.Models were constructed to show the relationship between RR, the number of cases, and the etiological fraction, reflecting the contribution of production factors to the development of occupational pathology.Strongly significant correlations were found between the impact factors (noise, dust, chemicals) and the morbidity rates of workers.A strong positive correlation was established between the level of inorganic dust (quartzite) and the frequency of upper respiratory diseases (r= 0.97, p<0.01), as well as between dust from inorganic materials (coke, phosphate, gypsum) and the frequency of upper respiratory diseases (r=0.84,p<0.01).The correlation between noise levels and sensory diseases (r=0.40,p<0.05) confirmed the impact of physical factors on workers health.Based on the obtained results, a matrix for assessing working conditions was developed, taking into account the new classification of harmful factors proposed in Kazakhstan.The results presented can be used for the prevention of occupational diseases and the optimization of working conditions at industrial enterprises.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".