Bibliographic record
Abstract
BACKGROUND: Women constitute a large percentage of the workforce in industrialized countries. As a result, addressing pregnancy-related health issues in the workplace is important in order to formulate appropriate strategies to promote and protect maternal and infant health. AIMS: To explore issues affecting pregnant women in the workplace. METHODS: A systematic literature review was conducted using Boolean combinations of the terms 'pregnant women', 'workplace' and 'employment' for publications from January 1990 to November 2010. Studies that explicitly explored pregnancy in the workplace within the UK, USA, Canada or the European Union were included. RESULTS: Pregnancy discrimination was found to be prevalent and represented a large portion of claims brought against employers by women. The relationship between environmental risks and exposures at work with foetal outcomes was inconclusive. In general, standard working conditions presented little hazard to infant health; however, pregnancy could significantly impact a mother's psychosocial well-being in the workplace. CONCLUSIONS: Core recommendations to improve maternal and infant health outcomes and improve workplace conditions for women include: (i) shifting organizational culture to support women in pregnancy; (ii) conducting early screening of occupational risk during the preconception period and (iii) monitoring manual labour conditions, including workplace environment and job duties.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".