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Record W4404554124 · doi:10.33137/utjph.v5i1.44235

Risk Factors Associated with Mood Disorders in Canada

2024· article· en· W4404554124 on OpenAlexaffabout
Yuchen Jiang, Rosane Nisenbaum

Bibliographic record

VenueUniversity of Toronto Journal of Public Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMental healthMedicineMoodPsychological interventionMood disordersPopulationObesityLogistic regressionOdds ratioOddsDemographyGerontologyPsychiatryEnvironmental healthAnxietyInternal medicine

Abstract

fetched live from OpenAlex

Background: Mental health presents a profound challenge globally, affecting around 970 million individuals. In Canada, mental disorders significantly impact the well-being of society and individuals, with one in three Canadians expected to face a mental problem during their lifetime. However, the utilization of mental health services is low, and a large proportion of the population does not have access to effective care. Objectives: This study was designed to identify key sociodemographic, clinical, and lifestyle factors associated with the prevalence of mood disorders among Canadians, thereby aiding in the development of targeted interventions. Methods: Utilizing data from the Canadian Community Health Survey 2017-2018, multivariable logistic regression analysis was performed, incorporating the sampling weights to make the inference representative of the Canadian population. Bootstrap weighting was applied to the multivariable associations to ensure robust variance estimates. Results: Females (OR, 1.98; 95% CI, 1.86-2.16) and individuals with higher obesity levels (pre-obesity: OR, 1.05; 95% CI, 1.04-1.05; obesity class 3: OR, 2.37; 95% CI, 1.88-3.00) were more likely to experience mood disorders. Conversely, higher income levels (>$80,000: OR, 0.57; 95% CI, 0.49-0.66; $60,000-$79,999: OR, 0.66; 95% CI, 0.56-0.77) and immigrant status (OR, 0.49; 95% CI, 0.43-0.56) were linked to a lower prevalence of mood disorders. Being unmarried was also associated with lower odds of mood disorders. Furthermore, additional factors such as higher education, increased life stress, and smoking were found to significantly influence the prevalence of mood disorders. Conclusions: The prevalence of mood disorders in Canada is influenced by various factors, with significant gender disparities. These findings can assist policymakers and healthcare professionals in developing targeted interventions and allocating resources effectively to meet the specific needs of at-risk populations. Future research is necessary to address these determinants.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.025
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.026
GPT teacher head0.269
Teacher spread0.243 · 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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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