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Record W2069846704 · doi:10.1016/s0924-9338(12)74503-0

P-336 - Family quality of life in asd and adhd

2012· article· en· W2069846704 on OpenAlexaff
Elena Predescu, Felicia Iftene

Bibliographic record

VenueEuropean Psychiatry · 2012
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsAutismExternalizationPsychologyClinical psychologyQuality of life (healthcare)DistressCoping (psychology)PopulationRutterPsychiatryDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

The impact of raising a child with autism on parents' quality of life (QOL) is not yet fully understood. Studies have shown that parents of children with autism have lower QOL than general population and that theirs QOL is influenced by the level of child functioning impairment, the received social support and by the use of maladaptive coping mechanisms. To evaluate the QOL of the families of children with autism compared with that of families of children diagnosed with ADHD. To analyze the factors which influence the QOL of these families. We used data from 60 children, aged between 2 and 18 years, diagnosed with ASD or ADHD, according to DSM IV-TR and ICD-10 and their parents. Tests were administered to assess the presence of autism symptoms, the symptoms of internalization / externalization and the parents’ emotional regulation mechanisms. FQOLS (Family Quality of Life Survey) was used to assess the subjects and their families’ quality of life. The areas with the lowest scores in terms of FQOL were the financial status, the support from others and the support from services. These results were similar for the families of children with ASD and ADHD. The level of parental distress was associated with the level of child internalizing problems. By understanding how the FQOL is affected by these disorders, the needs of the children with developmental disorders and their families will be better recognized and healthcare and support services would be appropriately developed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.112
GPT teacher head0.397
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

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