The Prevalence and Determinants of Unintended Pregnancies Among Women in Abakaliki, Southeast Nigeria
Bibliographic record
Abstract
BACKGROUND: Unintended pregnancy is a major social and public health problem affecting women within the reproductive age group. It jeopardizes women’s sexual and reproductive health and may pose a threat to the achievement of Sustainable Development Goal 3. Objective: To determine the prevalence and determinants of unintended pregnancy in Abakaliki, Southeast Nigeria. METHODS: A semi-structured questionnaire was used for a cross-sectional survey of antenatal clinic attendees at the Federal Teaching Hospital, Abakaliki from January 2015 to March 2015. A total of 185 questionnaires were correctly filled and analyzed using 2008 Epi Info version statistical software (Atlanta Georgia, USA). RESULTS: Out of the 185 antenatal clinic attendees, 43.8% (81/185) reported having had an unintended pregnancy at some point in their lives. The age at marriage, level of education, place of residence, sex education and use of contraception were significant determinants of unintended pregnancy. CONCLUSION: The prevalence of unintended pregnancy in this study was high. Its determinants include educational status, use of contraception, age at marriage and place of residence.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".