Social paediatrics and early child development: Part 1
Bibliographic record
Abstract
Diseases of modernism, rather than infectious diseases and chronic medical conditions, increasingly cause childhood morbidity and mortality. Thus, the goal of enhancing life outcomes for all children has become imperative. Paediatricans may begin with a renewed interest in social paediatrics - the care of the disadvantaged child in Canada, requiring a focus on all the complex factors that impact families and the community. New paediatricians need the tools to impact both social determinants of health and political policies to support health for all. Such interest is as old as the field of paediatrics (social medicine began with the great pathologist, Virchow, in the 1800s). The new neuroscience of experience-based brain and biological development has caught up with the social epidemiology literature. It is now known from both domains that a child's poor developmental and health outcomes are a product of early and ongoing socioeconomic and psychological experiences. In the era of epigenetics, it is now understood that both nature and nurture control the genome. Future paediatricians need to understand the science of experience-based brain development, and the interventions demonstrated to improve life trajectories. A challenge is to connect the traditional population health approach with traditional primary care responsibilities. New and enhanced collaborative interdisciplinary networks with, for example, public health, primary care, community resources, education and justice systems are required.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".