A Multilevel Analysis of the Contribution of Individual, Socioeconomic and Geographical Factors on Kindergarten Children’s Developmental Health: A Saskatchewan Province-Wide Study
Bibliographic record
Abstract
In current literature of child public health, a growing number of studies has been dedicated to early childhood development with a focus on child developmental health measured via the teacher completed Early Development Instrument (EDI). Using multilevel modeling as the optimal statistical method to analyze hierarchical EDI data, this study determines the strength of the effect and significance of predictors of children’s 5 EDI outcomes, vulnerability, and the multiple vulnerability by taking into account the hierarchy present in its design. In addition, this study conducts an extensive epidemiological review of the risk factors associated with a child’s developmental health at each level of the hierarchy, at cross-levels of the hierarchy and their variations across different levels of the hierarchy. This cross-sectional study considered 9045 Saskatchewan children who were ages 4-8 years in the 2008-2009 school years. Individual child characteristics, EDI domains, and vulnerability data were collected by the Ministry of Education teachers in the provincial 2008 EDI project; neighborhood contextual Census data were compiled by SPHERU staff at the University of Saskatchewan. Multilevel linear and logistic models were used to analyze the data. According to the results, individual characteristics, such as being Aboriginal, an ESL learner, male, and being absent from school; neighborhood characteristics such as income inequality; and geographical characteristics such as living in a large city have negative effects on EDI scores and exacerbating the odds of vulnerability. Compounding effects of Aboriginal-special skills, large city-Aboriginal, and large city-neighborhood median income were positive on the above outcomes with considerable either significance or strength, while those of neighborhood income inequality-Aboriginal, and large city-neighborhood income inequality were negative with notable significance and strength. Furthermore, neighborhood contextual variables contribute to a considerable proportion of health outcome variations and the results associated with neighborhood income inequality give further evidence of the income inequality hypothesis. The findings of this study recommend provincial child public health policy makers’ extended attention to Aboriginal children, children with ESL status, those children living in neighborhoods with high income inequality and children from Regina.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".