Blending Traditional and Nurturing Fathering: Fathers of Children With Autism Managing Work and Family
Bibliographic record
Abstract
Objective Against a backdrop of hegemonic masculinity, we contribute to understandings of how having a child with autism impacts fathers' navigation of work and family responsibilities. Background Parents of children with autism face distinct needs related to accessing health, education, and social supports for their children. In supporting their children, fathers may feel pulled between traditional financial provider roles and relatively nurturing, involved styles of fathering. Method Using a traditional masculinity theoretical orientation, we conducted a directed content analysis of narrative data from 26 fathers of children with autism collected as part of a broader project. We analyzed approaches to fathering reflected in fathers' descriptions of managing work and family and corresponding meanings fathers attached to work relative to family responsibilities. Results Fathering approaches included (a) traditional breadwinners, (b) caregiving breadwinners, (c) “tag‐team” parents, and (d) caregiving fathers. Meanings of work included (a) financial power and security in the face of autism; (b) work as information, support, and reprieve; and (c) work strain contributing to guilt, sadness, and depression. Conclusion Fathers' responsibilities entailed a careful balancing between financial provision and caregiving for their children with autism. We identify theoretical and policy implications aimed at more fully understanding and supporting fathers of children with autism.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".