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Record W1999870586 · doi:10.1037/a0016758

Mothers’ and fathers’ attributions for adolescent behavior: An examination in families of depressed, subdiagnostic, and nondepressed youth.

2009· article· en· W1999870586 on OpenAlexafffund
Lisa Sheeber, Charlotte Johnston, Mandy Chen, Craig Leve, Hyman Hops, Betsy Davis

Bibliographic record

VenueJournal of Family Psychology · 2009
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Mental HealthU.S. Public Health ServiceUniversity of British Columbia
KeywordsAttributionPsychologyDevelopmental psychologyDepressive symptomsClinical psychologyPsychiatryCognitionSocial psychology

Abstract

fetched live from OpenAlex

This study examined whether parents of adolescents experiencing depressive symptoms or disorder make more negative and fewer positive attributions for their adolescents' behavior than do parents of nondepressed adolescents, and whether parental attributions for adolescents' behavior contribute to parenting behavior, above and beyond the adolescents' behavior. Parents and adolescents (76 girls and 48 boys) participated in videotaped problem-solving interactions (PSIs). Each parent subsequently watched the videotape and offered attributions for their adolescent's behavior. In addition, parent and adolescent behavior during the PSIs was coded. Mothers and fathers in families of nondepressed adolescents made significantly fewer negative attributions for their children's behavior than did parents in families of adolescents with diagnostic or subdiagnostic levels of depressive symptoms. Moreover, mothers' and fathers' negative attributions were related to greater levels of observed aggressive behavior and lower levels of observed facilitative behavior during the PSIs controlling for both demographic characteristics and the relative level of adolescent aggressive and facilitative behavior during the PSI.

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)0.000
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 score0.455
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.062
GPT teacher head0.348
Teacher spread0.286 · 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

Citations45
Published2009
Admission routes2
Has abstractyes

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