Investigating social determinants of child health and their implications in reducing pediatric traumatic injury: A framework and 17-year retrospective case-control study protocol
Bibliographic record
Abstract
INTRODUCTION: Traumatic physical injuries are the number one cause of hospitalization and death among children in Canada. The majority of these injuries are preventable. The burden from injury can be reduced through prevention programs tailored to at-risk groups, however, existing research does not provide a strong explanation of how social factors influence a child's risk of injury. We propose a theoretical framework to better understand social factors and injury in children and will examine the association between these social factors and physical traumatic injury in children using large population-wide data. METHODS AND ANALYSIS: We will examine data from 11,000 children hospitalized for traumatic physical injury and 55,000 matched uninjured children by linking longitudinal administrative and clinical data contained at the Manitoba Centre for Health Policy. We will examine 14 social determinants of child health measures from our theoretical framework, including receipt of income assistance, rural/urban status, socioeconomic status, children in care, child mental disorder, and parental factors (involvement with criminal justice system, education, social housing, immigration status, high residential mobility, mother's age at first birth, maternal Axis I mental disorder, maternal Axis II mental disorder and maternal physical disorder) to identify groups and periods of time when children are at greatest risk for traumatic physical injury. A conditional multivariable logistic regression model will be calculated (including all social determinant measures) to determine odds ratios and adjusted odds ratios (95% confidence interval) for cases (injured) and controls (non-injured). ETHICS AND DISSEMINATION: Health Information Privacy Committee (HIPC No. 2017/2018-75) and local ethics approval (H2018-123) were obtained. Once social measures have been identified through statistical modelling, we will determine how they fit into a Haddon matrix to identify appropriate areas for intervention. Knowing these risk factors will guide decision-makers and health policy.
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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 | 0.024 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".