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Record W4404243865 · doi:10.1080/09638237.2024.2426983

Changes in sex differences in mental health over time: the moderating effects of educational status and loneliness

2024· article· en· W4404243865 on OpenAlexaffabout
Yeshambel T. Nigatu, Christine M. Wickens, Hayley A. Hamilton

Bibliographic record

VenueJournal of Mental Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsPublic Health OntarioUniversity of TorontoInstitute for Work & HealthCentre for Addiction and Mental Health
Fundersnot available
KeywordsLonelinessMental healthPsychologyClinical psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Background Limited evidence exists regarding how sex differences in mental health are changing over time, especially in the context of recent health and economic adversities.Aims To examine the temporal shifts in mental health issues among males and females, and explore the influence of education and loneliness on these trends.Methods Data were utilized from the 2020 to 2023 Monitor study, a repeated cross-sectional survey of adults 18 years and older in Ontario, Canada. The study employed a Qualtrics-based web panel survey (n = 5,317). Mental health was assessed using Kessler-6 questionnaire, and analyses were performed using Generalized Linear Model (GLM) with gamma distribution.Results The results showed that there was a significant three-way interaction effect between sex, time and education with psychological distress (p = 0.014), suggesting that psychological distress increased between 2020 and 2023 among males who had less than college education. However, it remained stable among males with college/university degrees and females overall. Interaction between sex and feeling lonely on psychological distress was also evident (p = 0.004).Conclusions Mental health issues remained a significant public health challenge among adults, especially psychological distress increasing among males with less than a college education. This underscores the importance of targeted interventions addressing males’ mental health.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.236
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.388
Teacher spread0.365 · 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 teacher head, 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

Citations0
Published2024
Admission routes2
Has abstractyes

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