HEALTH-RELATED FAMILY QUALITY OF LIFE WHEN A CHILD OR YOUNG PERSON HAS A DISABILITY
Bibliographic record
Abstract
Parents of a child or young person with disability face not only the same challenges as parents of typically developing children and young people, but also the extra challenges of supporting the child or young person with disability in such a way as to maximise both their own quality of life (QOL) and family quality of life (FQOL) for all family members. Health-related quality of life (HRQOL) encompasses not only physical health but also mental and emotional health, equally important for FQOL. This article builds on information from previous publications, and illustrates relevant issues and the innovative methods parents, caregivers, and professionals have devised to enhance the HRQOL for children and young people with disability, and to improve FQOL. The author draws upon her personal lived experiences of having two daughters, the eldest an adult with disability, as well as being the medical consultant and manager of a newly created health unit tasked with supporting students with disability, who often have high health needs, in educational settings. The health conditions selected are those that have a major impact, not only on the young person with disability but also on family members. Vignettes, all deidentified true stories, will be included to illustrate the multiple issues faced by children and young people with disability, their families and extended families, and treating clinicians. These stories will hopefully resonate with families in particular.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".