Exploring protective factors in a high-risk subsample: the pivotal role of paternal support in preventing depression in a cohort of young adults
Bibliographic record
Abstract
OBJECTIVE: Major depressive disorder (MDD) is a global concern due to its widespread prevalence and morbidity. It is crucial to identify protective factors in high-risk individuals, including those with a familial predisposition, maltreatment history, and socioeconomic vulnerabilities. METHODS: We assessed a high-risk subsample within a young adult population cohort (n = 791; mean age = 31.94 [standard deviation {SD} = 2.18]) across three waves, using multiple regression models to analyze higher education, feeling supported, spirituality, psychotherapy access, higher socioeconomic status, involvement in activities, cohabitation, and family unity in waves 1 and 2 and their association with MDD resilience at wave 3. RESULTS: In the high-risk group, MDD incidence was 13.7% (n = 24). Paternal support had a protective effect on MDD incidence (odds ratio [OR] = 0.366; 95% confidence interval [95%CI] 0.137 to 0.955; p = 0.040) and suicide attempt risk (OR = 0.380; 95%CI 0.150 to 0.956; p = 0.038). Higher resilience scores were also protective (OR = 0.975; 95%CI 0.953 to 0.997; p = 0.030), correlating with reduced Beck Depression Inventory (BDI) (r = 0.0484; B = -0.2202; 95%CI -0.3572 to -0.0738; p = 0.003) and Montgomery-Åsberg Depression Rating Scale (MADRS) scores (r = 0.0485; B = -0.2204; 95%CI -0.3574 to -0.0741; p = 0.003). CONCLUSION: Our paper emphasizes reorienting the MDD approach, focusing on positive prevention strategies. It highlights the crucial role of fathers in family-based interventions and in promoting resilience in high-risk populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".