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Record W2303498926 · doi:10.1177/1073191115607972

Parent and Adolescent Agreement for Reports of Life Stressors

2015· article· en· W2303498926 on OpenAlexaff
Shauna C. Kushner, Jennifer L. Tackett

Bibliographic record

VenueAssessment · 2015
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyStressorDevelopmental psychologyTemperamentNegative affectivityChild Behavior ChecklistClinical psychologyPsychopathologyPersonalitySocial psychology

Abstract

fetched live from OpenAlex

In this article, we investigated the extent and nature of informant discrepancies on parent- and adolescent self-report versions of a checklist measuring youth exposure to life stressors. Specifically, we examined (a) mean-level differences, relative consistency, and consensus for family-level and youth-specific stressors and (b) the utility of parent-youth discrepancies in accounting for variance in youth temperament and psychopathology. Participants were 106 parent-child dyads (47 male, 59 female; 90.6% mothers) aged 13 to 18 years old ( M = 16.01, SD = 1.29). The results revealed evidence for both congruence and divergence in parent and youth reports, particularly with respect to respondents' accounts of youth-specific stressors. Discrepancies for youth-specific stressors were associated with adolescents' negative affectivity, surgency, effortful control, and internalizing problems. Discrepancies for youth stressors may therefore reveal individual differences in emotionality and self-regulation, thus reflecting meaningful variance in adolescents' functioning.

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)0.015
metaresearch head score (Gemma)0.045
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 score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.073
GPT teacher head0.365
Teacher spread0.292 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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