Using Administrative Data to Identify Factors Associated with Healthy Development After Experiencing Household Challenge Adversity in Early Childhood
Bibliographic record
Abstract
This study aimed to identify factors associated with healthy development after experiencing household challenge adversity in early childhood. A cohort of 42,505 children born in Manitoba, Canada was created using linkable health, social, and education administrative data. Children were divided into 5 groups according to whether they experienced adversity between birth and four years of age in the following categories: 1) having a parent diagnosed with a mental health disorder, 2) having a parent diagnosed with a substance use disorder, 3) having a parent who received income assistance for at least 2 months, 4) experiencing at least one of the 3 adversities, and 5) having none of the adversities listed. For each group, multiple linear regression models were computed to determine the association of the independent variables (age, sex, birth order, team parenting, neighbourhood-level socio-economic status, parent high school completion, being an established resident in Canada, not having a major illness, residential stability, program participation, and connectedness to health care services) on child development, represented by their Early Development Instrument scores. In each group, factors associated with healthy development included at minimum: older age, being female, being firstborn, team parenting, higher neighbourhood-level socio-economic status, parent high school completion, being an established resident in Canada, and not having a major illness. Several of these factors are associated with increased access to material resources. As such, policies that facilitate the delivery of resources to lower-income families and neighbourhoods with lower socioeconomic status could translate into improved developmental outcomes for 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".