Resilience of Parents with a Child with Autism Spectrum Disorders and Factors for Its Potential Enhancement: Family Income and Educational Level
Bibliographic record
Abstract
Autism is linked to bodily and psychological challenges for the parents who raise autistic children, it creates increased stress levels and reduced resilience because of these factors. However, many parents face the additional problem of the weakness to cope with the expenses of each therapeutic intervention, whereas often they are not adequately educated so as to be able to manage properly the information about the treatment of autism and maybe also its intake as a compulsory condition for the family environment. The writer explored the effect of family income and the parents’ educational level on the reduction of resilience through a quantitative research based on a population sample that consisted of the parents of 312 autistic children in Greece, all of them couples, namely 624 men and women, divided in equal numbers. The important impact of the annual family income on the shaping of the levels of the characteristics under study is confirmed, as the result that was drawn is that the level of financial strength is positively connected to the social support the parents receive and the resilience they develop. Likewise, the level of education is linked to social support. The social support increases when the educational level is higher, on the contrary, the opposite goes for the levels of the total stress, parental distress, parent-child dysfunctional interaction, difficulty of child, which the lower educational level increases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".