A medical-financial partnership to address financial health for children with neurodevelopmental disability
Bibliographic record
Abstract
Beyond genetics and biology, a seemingly larger wave impacting children’s health today is modifiable and avoidable health inequities. These health disparities are often shaped by the social determinants of health, including social (e.g., racialized communities facing discrimination while accessing care), economic (e.g., families from low-income households experiencing food and housing insecurity), and/or environmental (e.g., families living in physical spaces with poor air and water quality) factors. These inequities can disproportionately and adversely impact marginalized groups, leading to differences in health outcomes and access to services. Thus, promoting health outcomes and reducing inequities for children requires a multi-faceted approach incorporating their environments across these spheres. Poverty has been emphasized as one of the most important social determinants for children’s health in high-income countries (1). Financial health is the root cause of other insecurities, such as housing insecurity, food insecurity, and energy insecurity. There are well-studied adverse health outcomes of childhood poverty, which begin in childhood and can continue over the life course. For example, poverty can impact birth weight, infant mortality, chronic illness, and nutrition (2). Specifically, the impact on neurodevelopment has been extensively studied where exposure to toxic stress mechanisms during sensitive stages of human development in childhood can impact brain development (2). Further, the Family Stress Model describes how poverty can negatively impact parenting through mechanisms of parental emotional wellbeing and parenting practices (3).
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.149 | 0.022 |
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".