Gender and sex differences in occupation-specific infectious diseases: a systematic review
Bibliographic record
Abstract
Occupational infectious disease risks between men and women have often been attributed to the gendered distribution of the labour force, with limited comparative research on occupation-specific infectious disease risks. The objective of this study was to compare infectious disease risks within the same occupations by gender. A systematic review of peer-reviewed studies published between 2016 and 2021 was undertaken. To be included, studies were required to report infectious disease risks for men, women or non-binary people within the same occupation. The included studies were appraised for methodological quality. A post hoc power calculation was also conducted. 63 studies were included in the systematic review. Among high-quality studies with statistical power (9/63), there was evidence of a higher hepatitis risk for men than for women among patient-facing healthcare workers (HCWs) and a higher parasitic infection risk for men than for women among farmers (one study each). The rest of the high-quality studies (7/63) reported no difference between men and women, including for COVID-19 risk among patient-facing HCWs and physicians, hepatitis risk among swine workers, influenza risk among poultry workers, tuberculosis risk among livestock workers and toxoplasmosis risk among abattoir workers. The findings suggest that occupational infectious disease risks are similarly experienced for men and women within the same occupation with a few exceptions showing a higher risk for men. Future studies examining gender/sex differences in occupational infectious diseases need to ensure adequate sampling by gender.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".