Fathers Raising Children with Autism Spectrum Disorder: Stories of Marital Stability as Key to Parenting Success
Bibliographic record
Abstract
Using media reports of high divorce rates among couples of children with ASD as a point of departure, our purpose in this paper is to examine how married fathers of children with ASD understand their marriages relative to the demands of ASD and in the context of media reports of elevated divorce rates among parents raising children with ASD. We begin with a review of select literature pertaining to the impact of ASD on marriages and we include a brief account of popular media portrayals of the influence of having a child with a developmental disability, and ASD in particular, on marriages. We then describe our qualitative examination of narrative interview data from 26 married fathers raising children with ASD aged 2-13 beginning with our theoretical anchoring in social comparison to focus our attention on how fathers compare themselves with media accounts of elevated divorce rates among parents of children and also with other hypothetical family configurations. Our findings are evidence of fathers' strong and strengthened commitments to marriages and we illustrate a re-purposing of inflated portrayals of divorce rates to shore up fathers' sense of their own effectiveness as husbands and fathers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".