Examining Variations in the Prevalence of Diagnosed Mood or Anxiety Disorders Among Migrant Groups in Ontario, 1995–2015: A Population-Based, Repeated Cross-Sectional Study: Examen des variations de la prévalence des troubles anxieux ou de l’humeur diagnostiqués chez les groupes de migrants en Ontario, 1995–2015 : une étude transversale répétée dans la population
Bibliographic record
Abstract
BackgroundInternational evidence on the frequency of mood or anxiety disorders among migrant groups is highly variable, as it is dependent on the time since migration and the socio-political context of the host country. Our objective was to estimate trends in the prevalence of diagnosed mood or anxiety disorders among recent (<5 years in Canada) and settled (5–10 years in Canada) migrant groups, relative to the general population of Ontario, Canada.MethodsWe used a repeated cross-sectional design consisting of four cross-sections spanning 5 years each, constructed using health administrative databases from 1995 to 2015. We included all Ontario residents between the ages of 16 and 64 years. We assessed differences in the prevalence of mood or anxiety disorders adjusting for age, sex, and neighbourhood-level income. We further evaluated the impact of migrant class and region of birth.ResultsThe prevalence of mood or anxiety disorders was lower among recent (weighted mean = 4.10%; 95% confidence interval [CI], 3.59% to 4.60%) and settled (weighted mean = 4.77%; 95% CI, 3.94% to 5.61%) migrant groups, relative to the general population (weighted mean = 7.39%; 95% CI, 6.83% to 7.94%). Prevalence estimates varied greatly by region of birth and migrant class. We found variation in prevalence estimates over time, with refugee groups having the largest increases between 1995 and 2015.ConclusionsOur findings highlight the complexity of mood and anxiety disorders among migrant groups, and that not all groups share the same risk profile. These results can be used to help inform health service allocation and the development of supportive programs for specific migrant groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".