Prenatal care of women who give birth to Children with Fetal Alcohol Spectrum Disorder in a universal health care system: A retrospective cohort study utilizing linkable administrative data
Bibliographic record
Abstract
IntroductionFetal Alcohol Spectrum Disorder (FASD) is a significant public health concern. Prenatal care (PNC) settings are integral to preventing prenatal alcohol exposure as physicians delivering PNC services are in a unique position to reduce alcohol consumption during pregnancy. However, few studies have investigated PNC use among women who drink during Objectives and ApproachAnalysis was conducted of women with children born in Manitoba between April 1, 1984 and Mach 31, 2012, with follow up till 2013 using linkable administrative and novel clinic data. Study group included women whose child(ren) were diagnosed with FASD from 1999 to 2012 (n=702) at a centralised FASD diagnostic clinic. Comparison group included women from the general population whose children who did not have an FASD diagnosis (n=2097), matched on the index child’s birthdate, postal code, and SES. Adequacy of PNC utilization was defined using the revised Graduated Index of Prenatal Care Utilization. ResultsThis is the first population-based study to investigate rates of PNC usage of women who have given birth to children with FASD. Rates of inadequate PNC were higher among the study group (adjusted RR 2.47, 95% CI 2.08 to 2.94), as well as no PNC (adjusted RR 3.55, CI 2.42 to 5.22). Among the study group 63% accessed PNC, with 59% receiving intermediate, adequate, or intensive PNC. Conclusion/ImplicationsResults represent opportunity for screening and brief intervention programs to be delivered in PNC health care settings, as well as outreach programs to facilitate the uptake of PNC among high risk women.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".