Inequalities in assisted reproduction technology utilisation between the G20 countries
Bibliographic record
Abstract
Large global inequalities in assisted reproduction technology (ART) utilisation have existed ever since the introduction of ART. The reasons for these inequalities are multifactorial and include national wealth and affordability, pronatalist policies, regulatory differences in provision, and sociocultural components such as racial, gender and educational inequalities. Examining ART utilisation across the largest world economies (G20 countries) in 2016 (the most recent year with publically available data) reveals significant inequality, which is highly correlated to gross domestic product per capita, a measure of national wealth, and to provision of government funding and/or insurance coverage for in vitro fertilisation and intracytoplasmic sperm injection. A strong negative correlation with the Gender Inequality Index is also noted. The gap in ART utilisation rate will only begin to close once the majority of nations introduce more affordable ART treatment, instigate pronatalist policies, and implement changes in education, attitudes and behaviours to minimise racial and gender inequalities; however, achieving all of these changes may be a very difficult target to attain for many poorer economies, regardless of their size.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".