To be, or not to be, referred: A qualitative study of women from Burma's access to legal abortion care in Thailand
Bibliographic record
Abstract
BACKGROUND: Reproductive health outcomes among women from Burma who live along the Thailand-Burma border demonstrate an unmet need for access to safe abortion services. In 2014, a multi-national team launched a collaborative three-year initiative to expand a program that refers eligible women for safe and legal abortion care to government Thai hospitals in Tak province, Thailand. METHODS: Over a six-month period we conducted 14 in-depth open-ended interviews with women from Burma who were referred through the program or denied a wanted abortion after being deemed ineligible for referral by staff at the participating clinic. We analyzed the interviews for content and themes using both deductive and inductive techniques. RESULTS: Women's experiences accessing legal abortion care were positive and facilitated by appropriate options counseling, logistical support, and financial coverage. Five of the ineligible women we interviewed used traditional methods accessed on both sides of the border to self-induce an abortion and/or visited an untrained and unregulated provider. DISCUSSION: Our findings highlight the need to redouble efforts to expand access to safe and legal abortion care for women from Burma residing in northern Thailand. Ensuring that women who are denied a safe and legal abortion receive harm reduction interventions and resources is critical.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".