A qualitative exploration of the reasons and influencing factors for pregnancy termination among young women in Soweto, South Africa: a Socio-ecological perspective
Bibliographic record
Abstract
BACKGROUND: Pregnancy termination is an essential component of reproductive healthcare. In Southern Africa, an estimated 23% of all pregnancies end in termination of pregnancy, against a backdrop of high rates of unintended pregnancies and unsafe pregnancy terminations, which contributes to maternal morbidity and mortality. Understanding the reasons for pregnancy termination may remain incomplete if seen in isolation of interpersonal (including family, peer, and partner), community, institutional, and public policy factors. This study therefore aimed to use a socio-ecological framework to qualitatively explore, in Soweto, South Africa, i) reasons for pregnancy termination amongst women aged 18-28 years, and ii) factors characterising the decision to terminate. METHODS: In-depth interviews were conducted between February to March 2022 with ten participants of varying parity, who underwent a termination of pregnancy since being enrolled in the Bukhali trial, set in Soweto, South Africa. A semi-structured, in-depth interview guide, based on the socioecological domains, was used. The data was analysed using reflexive thematic analysis, and a deductive approach. RESULTS: An application of the socio-ecological framework indicated that the direct reasons to terminate a pregnancy fell into the individual and interpersonal domains of the socioecological framework. Key reasons included financial dependence and insecurity, feeling unready to have a child (again), and a lack of support from family and partners for the participant and their pregnancy. In addition to these reasons, Factors that characterised the participants' decision experience were identified across all socio-ecological domains and included the availability of social support and (lack of) accessibility to termination services. The COVID-19 pandemic and resultant lockdown policies also indirectly impacted participants' decisions through detrimental changes in interpersonal support and financial situation. CONCLUSIONS: Amongst the South African women included in this study, the decision to terminate a pregnancy was made within a complex structural and social context. Insight into the reasons why women choose to terminate helps to better align legal termination services with women's needs across multiple sectors, for example by reducing judgement within healthcare settings and improving access to social and mental health support.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".