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Record W3079504271 · doi:10.2196/19168

Mental Health Professional Consultations and the Prevalence of Mood and Anxiety Disorders Among Immigrants: Multilevel Analysis of the Canadian Community Health Survey

2020· article· en· W3079504271 on OpenAlexaffvenueabout
Chinenye Nmanma Nwoke, Udoka Okpalauwaekwe, Hauwa Bwala

Bibliographic record

VenueJMIR Mental Health · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of SaskatchewanUniversity of Lethbridge
Fundersnot available
KeywordsMental healthAnxietyMoodMedicineMood disordersImmigrationPsychiatryMultilevel modelDepression (economics)Community healthClinical psychologyPublic healthGeographyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: There is a significant body of evidence on the link between migration and mental health stressors. However, there has been very little research on the use of mental health services by immigrants in Canada. The prevalence of mental health professional consultations among immigrants, as well as its correlations, are not well understood and remain largely unknown. OBJECTIVE: This study aims to examine how specialist mental health visits (to a psychiatrist) differ from general mental health visits (to a family doctor or general practitioner) from immigrants, when compared to visits from those born in Canada, in a nationally representative sample of Canadian adults. This study also examines which group-immigrant or Canadian-born-suffers more from depression or anxiety, 2 of the more common mental health conditions. METHODS: We used data from the Canadian Community Health Survey (CCHS) between the years 2015 and 2016. The outcome variables included consultation with any mental health professional, consultation with a specialist (psychiatrist), and the prevalence of mood and anxiety disorders. The independent variable was immigrant status. Other variables of interest were adjusted for in the analyses. Multilevel regression models were developed, and all analyses were performed with Stata IC statistical software (version 13.0, StataCorp). RESULTS: The prevalence of mood and anxiety disorders was significantly lower among immigrants compared with individuals born in Canada; the prevalence of mood disorders was 5.24% (389,164/7,422,773) for immigrants vs. 9.15% (2,001,829/21,885,625) for individuals born in Canada, and the prevalence of anxiety disorders was 4.47% (330,937/7,410,437) for immigrants vs. 9.51% (2,083,155/21,898,839) for individuals born in Canada. It is expected that individuals with a lower prevalence of mood or anxiety disorders would use mental health services less frequently. However, results show that immigrants, while less likely to consult with any mental health professional (OR=0.80, 95% CI 0.72-0.88, P<.001), were more likely to consult with a psychiatrist (OR=1.24, 95% CI 1.04-1.48, P=.02) for their mental health visits when compared to individuals born in Canada. CONCLUSIONS: The results of this study reveal an unusual discord between the likelihood of mental health professional consultations with any mental health professional and mental health visits with psychiatrists among immigrants compared to nonimmigrants in Canada. Mental health initiatives need to be cognizant of the differences in the associated characteristics of consultations for immigrants to better tailor mental health services to be responsive to the unique needs of immigrant populations in Canada.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.007
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.029
GPT teacher head0.367
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2020
Admission routes3
Has abstractyes

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