Prevalence and predictors of unintended pregnancy among women: an analysis of the Canadian Maternity Experiences Survey
Bibliographic record
Abstract
BACKGROUND: Unintended pregnancies (mistimed or unwanted during the time of conception) can result in adverse outcomes both to the mother and to her newborn. Further research on identifying the characteristics of unintended pregnant women who are at risk is warranted. The present study aims to examine the prevalence and predictors of unintended pregnancy among Canadian women. METHODS: The analysis was based on the 2006 Maternity Experiences Survey targeting women who were at least 15 years of age and who had a singleton live birth, between February 15, 2006 to May 15, 2006 in the Canadian provinces and November 1, 2005 to February 1, 2006 for women in the Canadian territories. The primary outcome was the mother's pregnancy intention, where unintended pregnancy was defined as women who wanted to become pregnant later or not at all. Sociodemographic, maternal and pregnancy related variables were considered for a multivariable logistic regression. RESULTS: Adjusted Odds Ratios (OR) and 95% Confidence Intervals (95% CI) were reported. Overall, the prevalence of unintended pregnancy among Canadian women was 27%. The odds of experiencing an unintended pregnancy were statistically significantly increased if the mother was: under 20 years of age, immigrated to Canada, had an equivalent of a high school education or less, no partner, experienced violence or abuse and had 1 or more previous pregnancies. Additionally, mothers who reported smoking, drinking alcohol and using drugs prior to becoming pregnant, were all associated with an increased likelihood of experiencing an unintended pregnancy. CONCLUSION: The study findings constitute the basis for future research into these associations to aid in developing effective policy changes and interventions to minimize the odds of experiencing an unintended pregnancy and its associated consequences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 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".