Social and Cultural Considerations in Family Quality of Life: Jewish and Arab Israeli Families' Child‐Raising Experiences
Bibliographic record
Abstract
Abstract This paper compares the family quality of life (FQOL) of families with a child with disability in Jewish and Arab communities in Israel. Social, political, and cultural realities have an effect on all members of society. For families with a child with disability there are additional challenges. Being a minority family with a child with a disability adds to the challenges. One hundred fifty‐eight Jewish and 105 Arab Israeli caregivers of children with disabilities responded to the Family Quality of Life Survey (FQOLS‐2006), which operationalizes FQOL as a construct of six measurement dimensions in nine core family life domains. Overall, Jewish families in Israel reported higher FQOL than Arab families. Although eight of the nine domains were rated highly for Importance, the main outcome measures Attainment and Satisfaction were rated lower for almost all domains. Some domains contributed to overall differences more than others. The patterns also differed for the Jewish and Arab families. It is important for policy and practice to consider the social/political status of families and to consider the possible impact this has on the family of a child with a disability. Further research is needed to develop application models for addressing the needs of minority populations in designing programs into the general service delivery system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".