Predictors of Health and Developmental Trajectories among Aboriginal Preschool-aged Children in Canada: Effects of Family- and Community-level Characteristics
Bibliographic record
Abstract
The purpose of this study is to examine the differences in major health trajectories between Aboriginal and non-Aboriginal children. We hypothesize that community conditions affect child health even when child and family characteristics are considered. In order to test this hypothesis, we identify a model of Aboriginal child health that consists of three broad conceptual categories of health determinants: community conditions, family environment, and child characteristics. By placing a strong emphasis on the community-specific determinants of health, the model provides an alternative paradigm that accentuates the role of geographic, cultural, socio-economic, and political contexts of Aboriginal child health. We propose to test the model by conducting a secondary data analysis on the National Longitudinal Survey of Children and Youth (Cycles 1-7) and the 1996-2006 Census of Canada Microdata filed. These files contain information on Aboriginal children, their families, and the communities in which they live. The results of this research will provide communities and governments the necessary information to identify priorities and set standards for programs and policies to enhance the health and well-being of Aboriginal children. Analysis of the longitudinal data will be conducted primarily through various latent growth curve modeling techniques within the context of structural equation modeling.\nPiotr Wilk is the Community Health Researcher/Educator at the Middlesex-London Health Unit, with a research focus on the health and well being of children. Dr. Wilk is currently conducting research on how the socio-economic conditions in which children are born and grow up affect their health and developmental trajectories. Dr. Wilk also focuses his research on health of Aboriginal children by examining the role of contextual predictors related to family characteristics and community/neighborhood characteristics. He is also involved in teaching advanced graduate courses in social statistics and quantitative research methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".