Expanding a cohort of children who had a response to the Household Food Security Survey Module: A novel approach using linked ICES administrative data
Bibliographic record
Abstract
ObjectiveThe Canadian Community Health Survey (CCHS) is a national cross-sectional survey, rich with household, health-related measures. Through linkage with administrative health services data, we aimed to investigate the relationship between household food-insecurity in childhood, and incident health conditions (asthma and diabetes). Linking mothers with children and children with siblings, provided a unique opportunity to expand our child cohort. ApproachWe linked five cycles of the CCHS with ICES administrative databases, to conduct longitudinal cohort studies of children <18 years who had a household response to the Household Food Security Survey Module (HFSSM). Three approaches were used to increase our sample of children: we included children who personally completed the HFSSM; the children of mothers who completed the HFSSM (via a unique database linking mothers with biological children); and the siblings of children who completed the HFSSM. We examined the characteristics of children from food secure and insecure households, accounting for the clustering of children living within the same home. In addition, we investigated the incidence of health outcomes using marginal time-to-event regression analysis to adjust for clinical and socio-economic confounders. Results and ConclusionsUsing novel linkages and multiple data sources we were able to study a large cohort of children (20,023 children whose mothers completed the HFSSM, 5,199 siblings of child respondents, 8,820 children who personally responded to the survey). This creative approach built capacity to investigate household-level determinants of health in a large cohort of Canadian children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".