Investigating the link between sleep and postpartum depression in fathers utilizing subjective and objective sleep measures
Bibliographic record
Abstract
BACKGROUND: While fathers are at risk of developing poorer sleep and depressive symptoms in the postpartum period, they represent an understudied population in the literature. The present study aimed to explore the association between sleep and postpartum depressive symptoms in fathers using subjective and objective sleep measures. METHODS: Fifty-four fathers reporting no history of depression took part in this cross-sectional study. At 6 months postpartum, paternal sleep was assessed for 2 weeks utilizing a self-report daily sleep diary, a self-report perceived sleep quality rating, and actigraphy. In the same period, depressive symptoms in fathers were assessed with the Center for Epidemiologic Studies-Depression Scale (CES-D). RESULTS: Regression analyses showed that paternal subjective sleep variables captured by the sleep diary (total nocturnal sleep time and number of night awakenings) were not related to postpartum depressive symptoms. However, self-reported perceived sleep quality was significantly associated with postpartum depressive symptom severity in fathers independently of demographic variables related to depression. Alternatively, the objective sleep variables (total nocturnal sleep time, number of night awakenings, sleep efficiency, and wake after sleep onset), measured by actigraphy, did not demonstrate a significant relationship with paternal depression scores. CONCLUSIONS: These findings highlight the importance of perceived sleep quality, along with better understanding its association with postpartum depressive symptoms. Implementing a multi-measure approach enabled us to expand our knowledge about how different facets of sleep relate to postpartum depression, specifically in fathers. The results have important implications for the development of clinical interventions targeting paternal sleep and mood in the postpartum period.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".