Factors associated with delayed diagnosis of mood and/or anxiety disorders
Bibliographic record
Abstract
INTRODUCTION: This study examined the association between time to diagnosis and sociodemographic and clinical characteristics as well as time to diagnosis and physical and mental health status, among Canadian adults with a self-reported mood and/or anxiety disorder diagnosis. METHODS: We used data from the 2014 Survey on Living with Chronic Diseases in Canada-Mood and Anxiety Disorders Component. The study sample (n=3212) was divided into three time to diagnosis subgroups: long (> 5 years), moderate (1-5 years) and short (< 1 year). We performed descriptive and multinomial multivariate logistic regression analyses. Estimates were weighted to represent the Canadian adult household population living in the 10 provinces with diagnosed mood and/or anxiety disorders. RESULTS: The majority (61.6%) of Canadians with a mood and/or anxiety disorder diagnosis reported having received their diagnosis more than one year after symptom onset (30.0% reported a moderate delay and 31.6% a long delay). Upon controlling for individual characteristics, we found significant associations between a moderate delay and having no or few physical comorbidities; a long delay and older age; and both moderate and long delays and early age of symptom onset. In addition, a long delay was significantly associated with "poor" or "fair" perceived mental health and the greatest number of activity limitations. CONCLUSION: These findings affirm that a long delay in diagnosis is associated with negative health outcomes among Canadian adults with mood and/or anxiety disorders. Time to diagnosis is particularly suboptimal among older adults and people with early symptom onset. Tailored strategies to facilitate an early diagnosis for those at greatest risk of a delayed diagnosis, especially for those with early symptom onset, are needed.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".