The Incidence of Methylphenidate Use by Canadian Children: What is the Impact of Socioeconomic Status and Urban or Rural Residence?
Bibliographic record
Abstract
OBJECTIVE: To examine socioeconomic, demographic, and behavioural factors that influence the incidence of methylphenidate use among children aged 4 to 13 years. METHOD: A total of 11,316 children, aged 2 through 11 years, from Cycle 1 (1994-95) of the National Longitudinal Survey of Children and Youth were followed up 2 years later in Cycle 2 (1996-97). The outcome measure was methylphenidate use in Cycle 2. Individual-level explanatory variables included sex, age, socioeconomic status (SES), mother's age at birth of child, lone-parent family status, parental working status, and hyperactivity-impulsivity and inattention probabilities. Area-level explanatory variables included income and rural or urban residence. We used hierarchical linear modelling to examine individual- and area-level factors that predicted methylphenidate use. RESULTS: The strongest predictors of methylphenidate use were behavioural: children with high hyperactive-impulsive and (or) inattention behaviours in 1994, compared with children low on these behaviours, were 4.5 to 6 times more likely to use methylphenidate 2 years later. SES remained a significant predictor of the incidence of methylphenidate use, even when other significant predictors were held constant, with lower SES being associated with higher use. Area-level income also predicted methylphenidate use. CONCLUSION: Even when children with similar behavioural symptoms and demographic characteristics were compared, socioeconomic factors had a significant impact on incidence of methylphenidate use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".