Family Quality of Life When There Is a Child With a Developmental Disability
Bibliographic record
Abstract
Abstract The conceptualization of individual quality of life is reasonably well established, and now family quality of life and intellectual disability is emerging as an important field of study. This article examines comparative family quality of life in three types of families: those with a child who has Down syndrome, those with a child with autism, and those of similar household composition but without a child with a disability. Data were collected using the Family Quality of Life Survey, which was sent to participating families, and by interviews with selected families on a follow‐up basis. Data from the 3 groups were analyzed in terms of quantitative and qualitative information. The needs and choices of families were contrasted in terms of the child’s diagnosis. Findings showed that families’ satisfaction and needs varied within the 9 quality of life domains assessed, raising questions of support and care and the ability of families to pursue desired goals. The authors suggest that there is a need to both identify and provide measures of care and support that would enable families to function at an optimum level within their home and community, so they may experience a quality life similar to that of families without a child with a disability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".