Buying a Better Baby: Unconditional Income Transfers and Birth Outcomes
Bibliographic record
Abstract
* Abbreviation: HBPB — : Healthy Baby Prenatal Benefit Can society buy a better infant? This is, in essence, the question posed in this month’s Pediatrics article by Brownell et al.1 Studying a group of low-income Canadian women who participated in an unconditional cash transfer program (Healthy Baby Prenatal Benefit [HBPB]), the authors compared the birth outcomes of participants with outcomes among a group of low-income nonparticipants. They found that the group that received the equivalent of $81.00 per month over the second and third trimesters of their pregnancies experienced a 21% decrease in the rates of low birth weight and a 17.5% decrease in the rates of prematurity among other clinically important outcomes. The findings are startling and raise some important policy questions. Few would disagree that society has a general interest in preventing low birth weight infants for many reasons, not the least of which is that the care they require is expensive to provide. Couple this recognized societal interest with the findings that poor women have disproportionately higher rates of delivering low birth weight infants and the argument for directing support to these women is highly persuasive. But how should that support be provided to be most effective? In 1 approach, custodians of society’s resources presume to know what … Address correspondence to Andrew D. Racine, MD, PhD, Albert Einstein College of Medicine and Montefiore Health System, 111 East 210th St, Bronx, NY 10467. E-mail: aracine{at}montefiore.org
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".