Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) remains a critical public health challenge globally. While tobacco smoking is the most recognized risk factor, occupational exposures-especially to vapors, gases, dusts, and fumes (VGDF)-are increasingly acknowledged as substantial contributors. This study offers a secondary reanalysis of publicly available Canadian data, originally collected through the occupational disease surveillance system (ODSS), to investigate COPD risk across diverse occupational sectors and gender strata. By transforming the original hazard ratio data into intuitive visual representations-including scatter plots, histograms, violin plots, and bar charts-we expose previously overlooked gender disparities and risk clusters. Notably, female workers in cleaning, textile, and food preparation services face equally high or even elevated risks compared to men in construction or manufacturing. Our findings underscore methodological limitations in prior studies-such as insufficient detail in indirect smoking adjustment, reliance on outdated occupational coding systems, and lack of individual-level variables-and emphasize the need for gender-sensitive surveillance, policy-oriented communication, and international data-sharing frameworks. This study reframes occupational COPD not only as a biomedical condition but as a social justice issue shaped by labor inequality and surveillance blind spots.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".