Child health, SES and family supports : an application of the Family Stress Model among toddlers in Canada
Bibliographic record
Abstract
Background: The Family Stress Model suggests that socioeconomic status (SES) and family supports (e.g., social supports, community supports and resources) are key predictors of child health. However, it is recognized that there is a gap in the literature applying this knowledge to young children. In this study, a modified version, where there was only a focus on three components of the Family Stress Model is applied to test the main and moderating effects among these variables and their ability to predict overall health in toddlers (ages 12-24 months). Purpose: The purpose of this study was to analyze and identify key findings from the Toddler Development Instrument (TDI) data regarding the associations between economic hardship (household income), social supports (access to family supports) and child health, using a modified Family Stress Model. Methods: Binary logistic regression was used to perform analysis on 803 surveys collected from participating parents of toddlers aged 12-24 months as part of the TDI pilot project data collected through the Human Early Learning Partnership (HELP) at the University of British Columbia (UBC). Data were collected from families across British Columbia, Canada at family and community centers, using convenience sampling. Results: Results showed that SES (household income) and access to family supports was significantly associated with child health. Additionally, access to family supports mediated the relationship between household income and child health. Moderating effects of access to family supports on the relationship of household income and child health were not found to be significant. Conclusions: The study findings support the Family Stress Model and add evidence to the literature that SES and access to family supports are predictors of child health. Attention to these predictors can help researchers, policy makers and providers prioritize areas of support for families with young 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".