“Without this program, women can lose their lives”: migrant women’s experiences with the Safe Abortion Referral Programme in Chiang Mai, Thailand
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it