Prevalence and characteristics of intended adolescent pregnancy: an analysis of the Canadian maternity experiences survey
Bibliographic record
Abstract
BACKGROUND: There is limited research focusing on adolescent women who intended to become pregnant, as majority of research examines unintended adolescent pregnancies. The objective was to examine the prevalence and characteristics of Canadian adolescent women who intended to become pregnant. METHODS: The analysis was based on the national 2006 Maternity Experiences Survey consisting of women who had a singleton live birth. The sample was restricted to adolescent women between 15 to 19 years of age. The main outcome of this study was the adolescent woman's pregnancy intention. A variety of sociodemographic, maternal, and pregnancy related factors were examined using a multivariable logistic regression. Adjusted odds ratios (OR) and 95 % confidence intervals (CI) were reported for all variables. RESULTS: The sample size was 290, weighted to represent 2224 adolescent women. Based on the adjusted model, the odds of experiencing an intended pregnancy were increased if the adolescent woman was between 18-19 years old (OR 2.62, 95 % CI 1.05, 6.57), had a partner (OR 2.37, 95 % CI 1.12, 4.99), experienced no violence/abuse (OR 3.08, 95 % CI 1.38, 6.86), and consumed no alcohol before pregnancy (OR 3.17, 95 % CI 1.56, 6.45). Additionally, adolescent women who reported drug use prior to pregnancy were more likely to have an intended pregnancy (OR 0.39, 95 % CI 0.16, 0.95). CONCLUSION: The findings from this study can be used as the basis for future research to investigate the characteristics and needs represented by this group of adolescents and to aid in the development of effective policies and programs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".