Association between prenatal care and small for gestational age birth: an ecological study in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: In Quebec, women living on low income receive a number of additional prenatal care visits, determined by their area of residence, of both multi-component and food supplementation programs. We investigated whether increasing the number of visits reduces the odds of the main outcome of small for gestational age (SGA) birth (weight < 10th percentile on the Canadian scale). METHODS: In this ecological study, births were identified from Quebec's registry of demographic events between 2006 and 2008 (n = 156 404; 134 areas). Individual characteristics were extracted from the registry, and portraits of the general population were deduced from data on multi-component and food supplement interventions, the Canadian census and the Canadian Community Health Survey. Mothers without a high school diploma were eligible for the programs. Multilevel logistic regression models were fitted using generalized estimating equations to account for the correlation between individuals on the same territory. Potential confounders included sedentary behaviour and cigarette smoking. The odds ratios (ORs) were adjusted for mother's age, marital status, parity, program coverage and mean income in the area. RESULTS: Mothers eligible for the programs remain at a higher odds of SGA than non-eligible mothers (OR = 1.40; 95% confidence interval [CI]: 1.30-1.51). Further, areas that provide more visits to eligible mothers (4-6 food supplementation visits) seem more successful at reducing the frequency of SGA birth than those that provide 1-2 or 3 visits (OR = 0.86; 95% CI: 0.75-0.99). CONCLUSION: Further studies that validate whether an increase in the number of prenatal care interventions reduces the odds of SGA birth in different populations and evaluate other potential benefits for the children should be done.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".