Why women choose abortion through telemedicine outside the formal health sector in Germany? A mixed-methods study
Bibliographic record
Abstract
Abstract Introduction In April 2019 the abortion telemedicine service Women on Web (WoW) opened their helpdesk to Germany and saw a progressive rise in consultations. Our aim was to understand the motivations, and perceived barriers to access, for women who choose telemedicine abortion outside the formal health sector in Germany. Methods We conducted a parallel convergent mixed-methods study among 1090 women in Germany, who requested medical abortion through WoW between January 1 st and December 31 st , 2019. We performed a cross-sectional study of data contained in online consultations and a content analysis of 108 email texts. Analysis was done until saturation; results were merged, and triangulation was used to validate results. Results Frequent reported reasons for choosing telemedicine abortion in the consultation forms were “I need to keep the abortion a secret from my partner or family” (48%) and “I would rather keep my abortion private” (48%). The content analysis developed two main themes and seven subsidiary categories: 1) internal motivations for seeking telemedicine abortion encompassing i) autonomy, ii) perception of external threat, iii) shame and stigma, and 2) external barriers to formal abortion care, encompassing: (iv) financial stress, v) logistic barriers to access vi) provider attitudes, and vii) vulnerability of foreigners). The findings in the quantitative and qualitative analysis were consistent. Conclusion Women in Germany who choose telemedicine abortion outside the formal health sector do so both from a place of empowerment and a place of disempowerment. Numerous barriers to abortion access exist in the formal sector which are of special relevance to vulnerable groups such as adolescents and undocumented immigrants. Key message points When Women on Web, an abortion telemedicine service operating outside the formal health care sector, opened in Germany in April 2019, 1205 women consulted the service in the first nine months. Women who choose telemedicine abortion do so both from a position of empowerment, for reasons of autonomy, and from a position of disempowerment and lack of autonomy. Numerous barriers to abortion access, as permitted by German law exist in the formal health sector, which may most impact vulnerable groups such as adolescents, women with low financial means, and undocumented immigrants.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".