Self‐reported mental health of mothers with a school‐aged child with a disability in Victoria: A mixed method study
Bibliographic record
Abstract
AIM: This research investigated the mental health of mothers of school-aged children with disabilities in Victoria, Australia. METHODS: A mixed method triangulation design model was used to investigate the mental health of mothers (n= 152) of school-aged children with developmental disabilities. Self-reported medical history and completion of the Short Form Health Survey Version 2 were used to collect data via mail-out survey and follow-up phone interview. RESULTS: Mothers reported subjective mental health two standard deviations below other Australians and higher rates of depression and anxiety that other Australian women and the adult population in general. Half of participants reported that their health affected their ability to provide the care that their child needed, and half experienced frequent interrupted sleep secondary to the care of their child with a disability. Significantly poorer mental health was reported by mothers with a pre-school-aged child as well as a child with a disability (P < 0.001), mothers with more than one child with a disability (P= 0.038), mothers of children with autism spectrum disorder (ASD) (P= 0.026), and mothers who recognised that their health affected care giving (P < 0.001). CONCLUSIONS: The reported mental health of participants in this study indicates that further attention is needed to action health strategies to support mothers of children with disabilities. Health programs and policy that will identify mothers in need of assistance, as well as management strategies that will adequately support mental wellness in mothers is required in Australia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".