The Shape of the Socioeconomic Gradient: Testing to Functional Form of the Relationship between Socioeconomic Status and Early Child Development
Bibliographic record
Abstract
IntroductionThe literature provides abundant evidence of socioeconomic gradients in health outcomes. However, it is unclear, and particularly understudied in early child development research, whether these observed gradients are linear, whether they diminish as socioeconomic status (SES) increases, and if they ultimately reverse in slope at the highest SES values. Objectives and ApproachWe linked neighbourhood-level Census and Tax Filer data with Early Development Instrument (EDI) data across Canada. The EDI is a kindergarten teacher-completed measure of five domains of early child development. We used this linked database to statistically compare and choose the most appropriate functional form of the relationship between each of the EDI domains (dependent variables), and the Canadian Neighbourhoods and Early Child Development (CanNECD) study's SES index (predictor) in regression models. Model comparison approaches included: visual checks of lines fitted using Generalized Additive Models, Akaike and Bayesian Information Criterions, Ramsay’s RESET, J and Cox tests. ResultsThe results indicate the optimal functional form of the gradient varies across domains of the EDI. The best model for the Physical Health and Well-Being domain was quadratic, suggesting there may be some reversal in slope at higher values of SES. The best models for the Social Competence and Language and Cognitive Development domains were logarithmic, indicating diminishing returns to SES but with no slope reversal. The best model for the Emotional Maturity domain was linear, suggesting the gradient was consistent across all values of SES. The best fit for the Communication Skills and General Knowledge domain was a cubic ‘S’ curve, suggesting the curve is positive and concave for lower levels of SES but curves upwards beyond a certain SES threshold. Conclusion/ImplicationsThe results demonstrate the importance of examining functional forms when modeling socioeconomic gradients. Assuming linear relationships between SES and health outcomes (early child development, in this case) may distort and bias the true nature of the relationships, thus leading to misinterpretations, especially at the highest and lowest values of SES.
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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 | 0.022 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".