35b Supporting women in mining: reproductive health, birth outcomes and breastfeeding
Bibliographic record
Abstract
Abstract Research and policies to protect workers from workplace hazards have generally focused on the male experience, and particularly the non-pregnant and non-breastfeeding worker. There are differences in how exposure to workplace hazards affect a woman and the fetus as well as the infant child when breastfeeding. These differences may put them at higher risk of negative health outcomes. Supporting women’s reproductive health is vital to providing decent work and advancing gender equality, and can contribute to increased recruitment and retention of women in the mining industry. Guidance is needed to best support pregnancies, birth outcomes and breastfeeding. Regulated and recommended occupational exposure levels established for the pregnant or breastfeeding employee are rare. Good guidance should include protective and meaningful work re-assignment when required. Based on literature reviews, potential or confirmed reproductive hazards in mining will be presented, including heat stress, metals, noise, vibration, ionizing radiation, ergonomics and shift work. Hazards will be presented along with their potential health outcomes, occupational exposure limits, when available, and control recommendations to mitigate risk. Gaps in information will be highlighted and the need for future research discussed. Protective measures to support women’s reproductive health in mining must include leadership support, training and awareness, written programs and policies, and accessible medical and emergency services. Integrating these elements into company management systems will be discussed.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".