“Without this program, women can lose their lives”: migrant women’s experiences with the Safe Abortion Referral Programme in Chiang Mai, Thailand
Bibliographic record
Abstract
For displaced and migrant women in northern Thailand, access to health care is often limited, unwanted pregnancy is common, and unsafe abortion is a major contributor to maternal death and disability. Based on a pilot project and situational analysis research, in 2015 a multinational team introduced the Safe Abortion Referral Programme (SARP) in Chiang Mai, Thailand, to reduce the socio-linguistic, economic, documentation, and transportation barriers women from Burma face in accessing safe and legal abortion care in Thailand. Our qualitative study documented the experiences of women with unwanted pregnancies who accessed the SARP in order to inform programme improvement and expansion. We conducted 22 in-depth, in-person interviews and analysed them for content and themes using deductive and inductive techniques. Women were overwhelmingly positive about their experiences using the SARP. They reported lack of costs, friendly programme staff, accompaniment to and interpretation at the providing facility, and safety of services as key features. Financial and legal circumstances shaped access to the programme and women learned about the SARP through word-of-mouth and community workshops. After accessing the SARP and receiving support, women became community advocates for reproductive health. Efforts to expand the programme and raise awareness in migrant communities appear warranted. Our findings suggest that referral programmes for safe and legal abortion can be successful in settings with large displaced and migrant populations. Identifying ways to work within legal constraints to expand access to safe services has the potential to reduce harm from unsafe abortion even in humanitarian settings.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".