From society to cell: Exploring the biological impacts of social exposures through linked biological and population-level child development data.
Bibliographic record
Abstract
ObjectivesOur objective was to capture a holistic view of a child from “society to cell” by building an inter-disciplinary data linkage between social, biological, and environmental factors to better understand how “experience gets under the skin” to influence social disparities in child development from conception onwards. ApproachLinking a unique combination of administrative, survey, and biological data, this research connects children’s epigenetic profiles and other biological markers (e.g., microbiome) to broader social determinants of health to examine the process of “biological embedding.” In British Columbia, Canada, the CHILD Study has collected biological data for a cohort of children from birth to age 5 (N = 840). Linked administrative records (e.g., health services, demographic data) provide key social and environmental information (e.g., parental depression, neighbourhood socio-economic status). Population-level child development data have been collected at age 5 using the Early Development Instrument and are linkable for a subset of this cohort (N ~ 250). ResultsThis unique linkage helps us understand the critical interplay between health outcomes and resulting disparities; how a child’s social environment may impact their biological makeup, and in turn their health and developmental outcomes due to influences on their neural, endocrine, and/or immune systems at the molecular level. Rather than limiting research to singular disciplines, this research aims to shift thinking around health problems towards a synthesized model to examine mechanisms and associations between early experiences and biological outcomes. We will discuss strengths and challenges of developing this linkage and working across disciplines such as medicine, education, and science to address research questions that span historically disparate areas of research. ConclusionAn interdisciplinary approach has been essential for the development and approvals of this project. Bringing together experts from diverse disciplines with different perspectives to use a novel approach enables us to better address the large and important issue of childhood social disparities and their enduring impact on life course health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".