Bibliographic record
Abstract
Background: New fathers are nearly twice as likely to experience depression than men in the general population. The majority of studies examining risk factors for paternal postpartum depression (PPD) have used cross-sectional data, while extant longitudinal studies are often limited in the time that fathers are followed as well as the frequency of assessments. There is a dearth of research that examines risk and protective factors related to differing trajectories of depression. Analysis of trajectories is advantageous as it does not assume that all fathers will experience the same course of depression and allows for unique predictors of each trajectory to be evaluated. Method: The current study recruited 160 fathers in the third trimester. Each participant completed a larger baseline survey followed by a depressive symptom questionnaire at 1, 3, 6, 9, and 12 months postpartum. Group-based semiparametric modelling was used to identify trajectories of paternal PPD; multinomial logistic regression was used to evaluate prenatal predictors of each trajectory. Results: A four trajectory solution was considered the best fitting model and most clinically informative. Higher insomnia symptoms, higher anxiety, and experiencing pregnancy complications predicted group membership in the “moderate-increasing symptoms” trajectory group, while lower insomnia symptoms, lower anxiety, and experiencing an unremarkable pregnancy were protective factors predicting group membership in the “no or minimal symptoms” group. Preliminary analysis of the “clinical-increasing symptoms” group suggested that higher anxiety and maternal anxiety significant predicted group membership when controlling for Type I error (p < .01). Discussion: The current study is the first to describe trajectories of PPD in Canadian men. These findings have the capacity to aide prenatal screening measures for men transitioning to parenthood, as well as early intervention strategies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".