Exploring the Links between Fathering, Masculinities and Health and Well-Being for Migrant Fathers: Implications for Policy and Practice
Bibliographic record
Abstract
Fathers’ uptake of paternity leave and care of children is shaped by various factors, including structural barriers and gender norms, which influence masculine identity formation. Such barriers to accessing leave and caring for children are thus influenced by a complex intersection of individual and institutional factors. Focusing on Australia, this article looks at migrant fathers’ decisions about parental leave and caregiving, and its intersection with gender (masculinities) and culture (race/ethnicity). We do so to unpack the structural barriers these men face, including those that influence their (mental) health and well-being. The authors identify a gap in research, and argue that there is a need to better understand the intersection of gender and culture on migrant fathers’ decisions to access parental leave and care for children. A better understanding of these decisions is integral to building better policy and programme supports for different groups of fathers and, ultimately, improving their mental health and well-being. It also identifies the need for research and policy to recognise the diversity of “migrant” fathers in both quantitative and qualitative research.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".