Why Pediatricians Need Lawyers to Keep Children Healthy
Bibliographic record
Abstract
Pediatricians recognize that social and nonmedical factors influence child health and that there are many government programs and laws designed to provide for children's basic needs. However, gaps in implementation result in denials of services, leading to preventable poor health outcomes. Physician advocacy in these arenas is often limited by lack of knowledge, experience, and resources to intervene. The incorporation of on-site lawyers into the health care team facilitates the provision of crucial legal services to vulnerable families. Although social workers and case managers play a critical role in assessing family stability and finding appropriate resources for families, lawyers are trained to identify violations of rights and to take the appropriate legal steps to hold agencies, landlords, schools, and others accountable on behalf of families. The incorporation of lawyers in the clinical setting originated at an urban academic medical center and is being replicated at >30 sites across the country. Lawyers can help enhance a culture of advocacy in pediatrics by providing direct legal assistance and case consultation for providers, as well as jointly addressing systemic issues affecting children and families. Until laws to promote health and safety are consistently applied and enforced, pediatricians will need lawyers to effectively care for vulnerable children.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".