Global Trends in Surrogacy Uptake by Destination and Nationality 2018–2020
Bibliographic record
Abstract
Background: Cross-border surrogacy arrangements are an increasingly common means to family building globally amongst gay male singles and couples, as well as infertile heterosexual singles and couples who do not have significant access to other routes to parenthood. Research has shown that when regulation or affordability in a home state is too restrictive people will travel across borders for surrogacy extensively. Establishing patterns of use has always been difficult. Accurate data is stymied by lack of documentation of international third-party reproductive care available to authorities. In addition, surrogacy destinations have been in a state of flux for some years. Estimating the trends in source and destination countries will improve understanding of the populations whose reproductive care needs cannot be met in their home country. Such data can assist regulators, policymakers and professionals to understand these trends and provide education and policy to minimise risk. Aim: Provide a quantitative estimate of the trends in relation to intended parents engaging in surrogacy arrangements per destination country, in the three calendar years 2018, 2019 and 2020, by country of residence. Method: Surrogacy agencies in Canada, Colombia, Cyprus, Georgia, Guatemala, Kenya, Mexico, Thailand, Ukraine and USA were asked to complete an anonymised online survey providing aggregate data on the number of commissioning singles and couples who contracted for surrogacy services during each year by nationality of the biological parent(s). Results: Over thirty agencies across eight nations reported on a total of 6550 clients commencing over these three calendar years. Clients from 96 source countries were represented. Results showed stark differences in the largest user nations of domestic and international surrogacy per capita, the most commonly used ‘destination’ countries and changes in patterns of engagement over time. Conclusion: Some source countries showed far higher rates of update of domestic or international surrogacy than others. Differences are likely associated with varying access to alternative family building pathways, differences in kinship norms, average incomes and the lack of availability of surrogates in their own country due to obstructive laws and/or cultural norms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".