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Record W2463080531 · doi:10.1037/dev0000143

Observed sensitivity during family interactions and cumulative risk: A study of multiple dyads per family.

2016· article· en· W2463080531 on OpenAlexaff
Dillon T. Browne, George Leckie, Heather Prime, Michal Perlman, Jennifer M. Jenkins

Bibliographic record

VenueDevelopmental Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDyadPsychologySiblingDevelopmental psychologyCognitionPsychosocialPsycINFOMaternal sensitivityMultilevel model

Abstract

fetched live from OpenAlex

The present study sought to investigate the family, individual, and dyad-specific contributions to observed cognitive sensitivity during family interactions. Moreover, the influence of cumulative risk on sensitivity at the aforementioned levels of the family was examined. Mothers and 2 children per family were observed interacting in a round robin design (i.e., mother-older sibling, mother younger-sibling and sibling-dyad, N = 385 families). Data were dyadic, in that there were 2 directional scores per interaction, and were analyzed using a multilevel formulation of the Social Relations Model. Variance partitioning revealed that cognitive sensitivity is simultaneously a function of families, individuals and dyads, though the importance of these components varies across family roles. Cognitive sensitivity for mothers was primarily attributable to individual differences, whereas cognitive sensitivity for children was predominantly attributable to family and dyadic differences, especially for youngest children. Cumulative risk explained family and individual variance in cognitive sensitivity, particularly when actors were older or in a position of relative competence or authority (i.e., mother to children, older to younger siblings). Overall, this study demonstrates that cognitive sensitivity operates across levels of family organization, and is negatively impacted by psychosocial risk. (PsycINFO Database Record

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 score0.077
Threshold uncertainty score0.894

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 head0.065
GPT teacher head0.326
Teacher spread0.261 · 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

Citations30
Published2016
Admission routes1
Has abstractyes

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