Bibliographic record
Abstract
Paternal perinatal mental health: Barriers to help-seeking Deborah Da Costa, PhD, Associate Professor at the Department of Medicine, McGill University, Scientist at McGill University Health Centre, details the benefits and barriers to paternity leave uptake by fathers following the birth of a child. The transition to parenthood can be a vulnerable time for men’s mental health. Yet there remains a lack of awareness regarding paternal perinatal mental health issues during this life stage. Mounting evidence indicates that a significant number of expectant and new fathers experience psychological distress, including depression and/or anxiety, during the perinatal period. (1,2) The prevalence of depression among fathers during the perinatal period is approximately 8% (1) and 10% for anxiety. (2) Untreated mental health conditions in fathers during the perinatal period can worsen paternal mental health status. (3) In addition, paternal perinatal depression can negatively impact maternal mental health (4) and adversely affect the child’s behavioural, emotional, cognitive, and physical development. (5) This underscores the importance of prevention and early intervention to promote the mental health of men during the transition to parenthood. The usefulness of such efforts, however, is contingent upon men’s willingness to access services to address their mental health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".