Characteristics of women with unplanned pregnancy: results from Botswana AIDS Impact Survey IV
Bibliographic record
Abstract
Background: In sub-Saharan Africa, there are 40 million pregnancies each year. Of these, one-quarter are unplanned and have negative health and socio-economic consequences. Aim: The aim of this article is to share findings on demographic characteristics of childbearing women who reported unplanned pregnancy. Methods: Botswana AIDS Impact Survey IV data were used to respond to the question, ‘What are the demographic characteristics of women who reported unplanned pregnancy in Botswana?’ The data extracted related to 311 687 women who responded to the question, ‘Have you given birth in the past 5 years?’ The authors then focused on data for 160 482 women who responded to the question ‘Was the last pregnancy planned?’ Using SPSS version 22, frequencies, means and standard deviations were calculated and demographic characteristics were analysed. Findings: More women with low educational status, unemployed, never married or cohabiting and residing in rural areas reported unplanned pregnancy. Conclusions: Unplanned pregnancy in Botswana requires targeted interventions.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".