The trajectory of maternal and paternal fatigue and factors associated with fatigue across the transition to parenthood
Bibliographic record
Abstract
BackgroundFatigue is prevalent in new parents and is associated with poorer functional performance and cognitive functioning. This can be particularly detrimental during the transition to parenthood when parents are adapting to new roles and demands. Examining the course of fatigue and related factors can provide important avenues for intervention and prevention.MethodsIn this longitudinal study, we assessed fatigue and its correlates in 108 mother/father couples. Multilevel modelling examined the prevalence and trajectory of fatigue across the transition to parenthood, as well as factors associated with post‐partum fatigue. Parents completed measures of fatigue, prenatal stress, depression and health, and post‐natal parental sleep quality, infant sleep duration, and infant negativity.ResultsMothers' and fathers' fatigue increased following the birth of their infant and remained at high levels. Poor sleep quality, stress, and depression were associated with maternal and paternal fatigue, while infant characteristics were more strongly associated with maternal fatigue. Prenatal depressive symptoms, parental sleep quality, infant sleep duration, and the interaction of gender by prenatal fatigue predicted post‐natal fatigue in our model.ConclusionOur results highlight the need for health professionals to educate new parents about fatigue and its management beyond the prenatal period. As correlates of fatigue for mothers and fathers differ, we need to expand our understanding of paternal fatigue and develop interventions tailored to their unique experiences.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".