Recent Trends in the Prescribing of ADHD Medications in Canadian Primary Care
Bibliographic record
Abstract
Objective: The aim of this study was to describe the prevalence and incidence of ADHD medication prescribing, by age and gender, from 2005 to 2015 in Canadian primary care. Method: A population-based retrospective cohort study was conducted to evaluate the prescribing of ADHD medications between 2005 and 2015 using electronic medical record data. Yearly prevalence and incidence of ADHD medication prescribing were calculated for preschoolers (up to 5 years old), school-aged children (6-17 years old), and adults (18-65 years old) along with a description of the types of ADHD medications prescribed between 2005 and 2015. Results: Between 2005 and 2015, there was a 2.6-fold increase in the prevalence of ADHD medication prescribing to preschoolers, a 2.5-fold increase in school-aged children, and a fourfold increase in adults. There was a corresponding rise in incidence of prescribing although this rise was moderate and estimates were much lower compared with prevalence. The most commonly prescribed medication was Methylphenidate (65.0% of all ADHD medications prescribed). Conclusion: Although the prevalence of ADHD has remained stable over time, this study found an increase in the prescribing of ADHD medications in all age groups between 2005 and 2015. Incidence of new prescriptions was small relative to prevalence, suggesting that longer term treatments are being adopted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".