Risk of Low Birth Weight According to Household Composition in Brussels and Montreal: Do Income Support Policies Variations Explain the Differences Observed between Both Regions?
Bibliographic record
Abstract
Variations in social policy between countries provide opportunities to assess the impact of these policies on health inequities. This study compares the risk of low birth weight in Brussels and Montreal, according to household composition, and discusses the impact of income support policies. For each context, we estimated the impact of income support policies on the extent of poverty of welfare recipients, using the model family method. Based on the differences found, we tested hypotheses on the association between low birth weight and household composition, using administrative data from the birth register and social security in each region. The extent of poverty of welfare families differs according to household composition. In Quebec, the combination of low welfare benefits and larger family allowances widens the gap between households with children and those without children. The risk of LBW also differs between these two contexts according to the number of children. Compared to children born into large welfare families, first-born children are more at risk in Montreal than in Brussels. In addition to the usual comparative studies on the topic, our study highlights the importance of an evaluative perspective that considers the combination of different types of income support measures to better identify the most vulnerable households.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".