Inequitable health service use in a Canadian paediatric population: A cross‐sectional study of individual‐ and contextual‐level factors
Bibliographic record
Abstract
BACKGROUND: Health service use may be influenced by multilevel predisposing, enabling, and need factors but is equitable when driven by need. The study's objectives were as follows: (a) to investigate residential context's effect on child health service use and (b) to examine inequity of child health service use by testing for effect measure modification of need factors. METHODS: The sample of 1,451 children was from a prenatal cohort recruited from London, Ontario, between 2002 and 2004, with follow-up until children were toddler/preschooler-aged. Individual-level data were linked by residential address to neighbourhood contextual-level data sourced from Statistics Canada. Multilevel logistic regression modelled factors associated with child health service use. Interaction terms were included in the model to test for effect measure modification of need factors by predisposing and enabling factors. RESULTS: Contextual-level factors were not associated with child health service use. Maternal parity and nativity to Canada modified the effect of the need factor, paediatric health condition, on health service use. Health condition's effect was lowest in children of Canadian-born mothers with one child only (OR = 1.58, p = .04) and highest in children of Canadian-born mothers with three or more children (OR = 3.52, p < .01). Further, its effect was higher in children of Canadian-born mothers compared to children of mothers who migrated to Canada; however, odds ratios were not statistically significant for the latter. CONCLUSIONS: Results may inform future investigation of the potential inequity of health service use for subgroups of children whose mothers are of lower parity and not Canadian-born. An understanding of these inequities may inform future healthcare policy and care for paediatric populations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".