Social Disadvantage Gets Inside The Family: Exploring Biopsychosocial Family Processes in the Emergence of Developmental Health Inequalities
Bibliographic record
Abstract
Children raised in socially disadvantaged settings often exhibit poorer developmental health in a variety of domains, including socioemotional, medical and cognitive functioning. This relationship appears to operate in part via a cascade linking poverty and cumulative environmental adversity to early biological risk and interpersonal family process. Moreover, these mechanisms are suggested to function over-and-above the direct effects of material investments in childrenâ s healthy development and wellbeing. Despite this knowledge, there remains a need for longitudinal developmental research examining the nature in which environmental risk in early life impacts multiple and distinct intermediary mechanisms that are manifest within the proximal family environment. In order to address this limitation, the present dissertation provides three separate empirical studies outlining the nature in which social disadvantage impacts family process (in the form of interpersonal sensitivity), and early biological risk (in the form of birth weight). Data for the present dissertation came from the Kids, Families and Places Study, which is a population-based prospective birth cohort of newborn infants and their families from Toronto and Hamilton, Ontario, Canada (N=501 families). The present dissertation demonstrates that social disadvantage (1) impacts cognitive functioning in a number of domains at the time of school entry via maternal sensitivity and material investments, (2) disrupts the receipt of cognitive sensitivity during family interactions across multiple dyads, particularly for younger siblings, and (3) simultaneously increases developmental risk for multiple siblings-per-family and tends to increase sibling differences in developmental experience. To conclude, implications for the study of developmental health and family science are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".