Abortion hotlines around the world: a mixed-methods systematic and descriptive review
Bibliographic record
Abstract
Barriers to access abortion services globally have led to the development of alternative methods to assist and support women who seek an abortion. One such method is the use of hotlines, currently utilised globally for abortion care. This review aimed to understand (1) how abortion hotlines facilitate access to abortion; and (2) how women and stakeholders describe the impact of hotlines on abortion access. Published quantitative and qualitative studies and grey literature were systematically reviewed alongside an identification and description of abortion hotlines in the public domain. Our findings highlight that the existence of abortion hotlines is highly context-dependent. They may exist either as an independent community-based model of care, or as part of formal care pathways within the health system. Hotlines operating in contexts with legal restrictions seem to be broader in scope and will use innovative approaches to adapt to their setting and reach hard-to-reach populations. All the abortion hotlines that provided information on a data extraction form used evidence-based guidelines but women seeking medical abortion still struggle to access quality medications. There is limited data in general on abortion hotlines, especially on the user and provider experience. Abortion hotlines have the potential to facilitate access to safe abortion care through evidence-based information and to decrease maternal mortality and morbidity from unsafe abortions for women and girls globally.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".