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Record W2588768714 · doi:10.5539/ijps.v9n2p1

Investigation of the Correlation of Family Resilience of Parents with a Child with Autism Spectrum Disorders, Parenting Stress and Social Support

2017· article· en· W2588768714 on OpenAlexvenueno aff
Paschalis Kavaliotis

Bibliographic record

VenueInternational Journal of Psychological Studies · 2017
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsSeriousnessPsychologyAutismPsychological resilienceDevelopmental psychologyCorrelationMental healthMeaning (existential)Clinical psychologyPsychiatrySocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Stress is an extremely serious symptom in the care of an autistic child and it deteriorates mainly in women and depending on the seriousness of the autism symptoms. As a result, physical and mental health problems are caused in the children’s carers, having as a consequence the attack on family resilience. Social support, standard or non-standard, seems that it can be related to the parents’ reduced stress levels as well as to an increase in resilience, even though not all researches agree on this conclusion. The correlation of these parameters in this study comes from a wider quantitative research, the sample of which were the parents of 312 autistic children in Greece, all of them couples, namely 624 men and women, divided in equal numbers. The scales’ correlations in this research showed a strong correlation of the family communication and problem solving with the utilization of resources that concern the social and financial field, and also with important management strategies of autism, such as maintaining a positive outlook and the ability to make meaning of adversities.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.084
GPT teacher head0.403
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2017
Admission routes1
Has abstractyes

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