Physical Health Co-occurrences in Canadians with Attention-Deficit/Hyperactivity Disorder: A Cross-Sectional Study
Bibliographic record
Abstract
Increased rates of physical health conditions in individuals with attention deficit/hyperactivity disorder (ADHD) have been reported across multiple countries. In this study, we sought to identify whether Canadians with ADHD have greater rates of various physical health conditions than Canadians without ADHD. Data were extracted from the national 2022 Mental Health and Access to Care Survey, collected by Statistics Canada. Individuals who indicated a diagnosis of ADHD were matched with respondents without ADHD based on demographic characteristics, resulting in a final sample size of 930 participants. Chi-squared statistics and odds ratios were used to identify differences in rates of thirteen physical health conditions between Canadians with and without ADHD. Canadians with ADHD were more likely to have a co-occurring diagnosis of asthma, arthritis, back problems, fibromyalgia, migraines, chronic lung conditions, bowel diseases, chronic fatigue, chemical sensitivities, and prior diagnoses of high blood pressure. No differences were found in rates between Canadians with and without ADHD for diagnoses of current high blood pressure, diabetes, heart disease and cancer. Canadians with ADHD, like others across the globe with ADHD, are at an increased risk of multiple physical health conditions. To promote early identification and intervention of mental and physical health concerns, efforts should be made to integrate services and educate health care providers to investigate the presence of physical health conditions in those with ADHD, and vice versa.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".