Gender Inequalities in Employment of Parents Caring for Children With Autism Spectrum Disorder in China: Cross-Sectional Study
Bibliographic record
Abstract
Background: The increasing need for child care is placing a burden on parents, including those with children with autism. Objective: The aim of this study was to examine the employment status of Chinese mothers and fathers with children with autism spectrum disorder (ASD), as well as to investigate the factors that affected their employment decisions. Methods: An online national survey was completed by the parents of 5018 children and adolescents with ASD aged 2-17 years (4837 couples, 181 single mothers, and 148 single fathers). The dependent variable was employment status-whether they kept working or quit to take care of their child. The independent variables were those characterizing the needs of the child and the sociodemographic characteristics of the family. Results: The employment rate of mothers with children and adolescents with ASD was 37.3% (1874/5018), while 96.7% (4823/4988) of fathers were employed. In addition, 54.3% (2723/5018) of mothers resigned from employment outside the home to care for their children, while only 2.8% (139/4988) of fathers resigned due to caring obligations. Mothers' employment was positively associated with their single marital status, lower educational level, and having assistance from grandparents. Having the grandparents' assistance was positively associated with fathers' employment. Conclusions: Gender inequalities in employment exist in China. Mothers caring for children with ASD had lower workforce participation than fathers. More female-friendly policies and a stronger gender equality ideology would be of benefit to Chinese society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".