Prevalence and predictors of teenage pregnancy in Pakistan: a trend analysis from Pakistan Demographic and Health Survey datasets from 1990 to 2018
Bibliographic record
Abstract
Abstract Background Teenage pregnancies carry an increased risk of adverse obstetric and health outcomes for mothers and children. Methods This study assessed the prevalence and predictors of teenage pregnancies over time in Pakistan using the Pakistan Demographic and Health Survey (PDHS). Data on 400 076 ever-married pregnant women aged 15–49 y from four PDHS datasets were used. Teenage pregnancy was the outcome variable, whereas a woman's and her partner's education, occupation, wealth quintile, region, place of residence and access to knowledge on family planning were the explanatory variables. Pooled prevalence was estimated and regression analysis was undertaken to produce an adjusted prevalence ratio with 95% CIs. Results Although the prevalence of teenage pregnancy decreased from 54.4% in 1990–1991 to 43.7% in 2017–2018, the pooled prevalence was 42.5% (95% CI 37.9 to 49.6%). The prevalence of teenage pregnancy was significantly associated with place of residence, wealth quintile, education and occupation. Conclusion Despite a growing focus on women's education, access to sexual and reproductive health (SRH) services and contraception in the last decade in Pakistan, the prevalence of teenage pregnancy is still high. There is a pressing need to develop appropriate strategies for increasing access to education, SRH services and use of contraception in Pakistan.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".