Unmet contraceptive needs among refugees: Crossroads Clinic experience
Bibliographic record
Abstract
Abstract Objective To describe what women of reproductive age who received primary care at a refugee health clinic were using for contraception upon arrival to the clinic, and to quantify the unmet contraceptive needs within that population. Design Retrospective chart review. Setting Crossroads Clinic in downtown Toronto, Ont. Participants Women of reproductive age (15 to 49 years) who first presented for care between December 1, 2011, and December 1, 2012. To be included, a woman had to have had 2 or more clinic visits or an annual health examination. Exclusion criteria for the contraception prevalence calculation were female sexual partner, menopause, hysterectomy, pregnancy, or trying to conceive. Main outcome measures Contraception use prevalence was measured, as was unmet contraceptive need, which was calculated using a modified version of the World Health Organization’s definition: the number of women with an unmet need was expressed as a percentage of women of reproductive age who were married or in a union, or who were sexually active. Results Overall, 52 women met the criteria for inclusion in the contraceptive prevalence calculation. Of these, 16 women (30.8%) did not use any form of contraception. Twelve women were pregnant at some point in the year and stated the pregnancy was unwanted or mistimed. An additional 14 women were not using contraception but had no intention of becoming pregnant within the next 2 years. There were no women with postpartum amenorrhea not using contraception and who had wanted to delay or prevent their previous pregnancy. In total, 97 women were married or in a union, or were sexually active. Unmet need was calculated as follows: (12 + 14 + 0)/97 = 26.8%. Conclusion There was a high unmet contraceptive need in the refugee population in our study. All women of reproductive age should be screened for contraceptive need when first seeking medical care in Canada.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".