The influence of social determinants of health on child physical health in Greater Sudbury neighbourhoods
Bibliographic record
Abstract
There is increasing awareness that social determinants of health are associated with growing health inequities, or avoidable differences, among many populations. The City of Greater Sudbury is experiencing these health inequities, including inequities in child physical health and wellbeing. This study will examine the relationship between specific social determinants of health and child physical health and wellbeing in Greater Sudbury neighbourhoods. The goals of this research are 1) to explore the relationships between specific social determinants of health and child physical health and wellbeing in Greater Sudbury neighbourhoods, 2) explore the collective influence of social determinants of health on child physical health and wellbeing, and 3) examine unique relationships that may exist between the social determinants of health and children physical health in neighbourhoods for the City of Greater Sudbury. The complexity, nature, and interactions of the social determinants of health within society makes observing them quantitatively difficult. This requires many different social determinants of health to be studied separately from one another, as well as together, in order to understand how they influence child physical health and wellbeing. In order to better understand these interactions, the social ecological model of health promotion presents an ideal theoretical framework for examining multiple variables and their correlations and, therefore, is used in this study. This study is an ecological crosssectional study using secondary data analysis of the 2011 National Household Survey (Statistics Canada) and the Early Development Instrument which was developed by the Offord Centre for Child Studies. This study involves a multi-variate analysis with the dependent variable of child physical health being represented by a composite measure of child physical health and wellbeing, and multiple independent variables including different measures of neighbourhood income, education, unemployment, lone-parent families and poverty. Child physical health and wellbeing is represented by the Early Development Instrument (EDI) - a questionnaire completed by the teacher or an Early Child Educator (ECE) when the child is in senior kindergarten. The EDI is a comprehensive measure of child physical health and wellbeing because it includes gross/fine motor skills, physical readiness for the school day, and physical independence. The social determinants of health are represented by the National Household Survey – a voluntary sample survey using a random sample collected by Statistics Canada, which the federal government uses to collect social and economic data about the Canadian population (Statistics Canada, 2011). IV Descriptive statistics address the assumptions of linear regression as well as examine the nature and normalcy of the independent and dependent variables. Then the presence of outliers are tested using univariate, bivariate, and multivariate detection methods. Linear and multiple regression tests are then used to analyze the influences of the social determinants of health on child physical health and wellbeing. The results of this study demonstrate the challenges of exploring geographical differences in the health of a population, and how those differences in health may be socially produced. Furthermore, this study provides insight into better understanding how child physical health and wellbeing in Greater Sudbury neighbourhoods may be influenced by socially produced health disparities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".