Changes in sex differences in mental health over time: the moderating effects of educational status and loneliness
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".