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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".