Decreased odds of depressive symptoms and suicidal ideation with higher education, depending on sex and employment status
Bibliographic record
Abstract
BACKGROUND: Higher education is associated with reduced depressive symptoms and requires investment without guaranteed employment. It remains unclear how sex and employment status together contribute to the association between mental health and educational attainment. This study investigated the role of sex and employment status together in the associations of 1) depressive symptoms and 2) suicidal ideation with education. METHODS: Using 2005-2018 National Health and Nutrition Examination Survey data, cross-sectional analyses were conducted on individuals ≥20 years who completed the depression questionnaire and reported their employment status and highest level of education. Survey-weighted multivariable logistic regression models were used to explore how depressive symptoms and suicidal ideation are associated with educational attainment in an analysis stratified by sex and employment status. To account for multiple testing, a significance level of a < 0.01 was used. RESULTS: Participants (n = 23,669) had a weighted mean age of 43.25 (SD = 13.97) years and 47% were female. Employed females (aOR = 0.47, 95% CI 0.32, 0.69), unemployed females (aOR = 0.47, 95% CI 0.29, 0.75), and unemployed males (aOR = 0.31, 95% CI 0.17, 0.56) with college education had reduced odds of depressive symptoms compared to those with high school education. Employed females with college education also had reduced suicidal ideation odds compared to those with high school education (aOR = 0.41, 95% CI 0.22, 0.76). CONCLUSIONS: Females demonstrated significant associations between depressive symptoms and education, regardless of employment status, whereas males demonstrated an association only if unemployed. Employed females, in particular, demonstrated a significant association between suicidal ideation and education. These findings may inform future research investigating the underlying mechanisms and etiology of these sex-employment status differences in the association between mental health and education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".