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Positive Experiences of Mothers and Fathers of Children with Autism

2010· article· en· W2017269007 on OpenAlexaff
Adam D. Kayfitz, Marcia N. Gragg, Rhonda Orr

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2010
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAutismPsychologyDevelopmental psychologyPositive parentingContext (archaeology)Child rearingPopulationRaising (metalworking)Clinical psychologyMedicinePsychiatryIntervention (counseling)

Abstract

fetched live from OpenAlex

Background The present study examined the positive experiences of parents raising school‐aged children with autism within the context of parenting stress. Materials and Methods Participants included 23 mother/father pairs raising children with autism (ages 5 to 11 years, M = 7.39). Parents completed measures of parenting stress and positive experiences of raising their children. Results Consistent with previous research in a pre‐school aged population of children with autism, mothers reported significantly more positive experiences than did fathers. Mothers’ and fathers’ reports of their positive experiences were negatively related to their reports of parenting stress. Fathers’, but not mothers’, positive experiences were negatively related to their partners’ reports of parenting stress. Conclusion Findings are discussed within a positive psychology framework suggesting that a focus on positive experiences may buffer against negative well‐being.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.052
GPT teacher head0.372
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 designQualitative
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

Citations186
Published2010
Admission routes1
Has abstractyes

Explore more

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