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Record W2599633661 · doi:10.1037/fam0000308

Familial risk and sibling mentalization: Links with preschoolers’ internalizing problems.

2017· review· en· W2599633661 on OpenAlexafffund
Michelle Rodrigues, Noam Binnoon-Erez, Heather Prime, Michal Perlman, Jennifer M. Jenkins

Bibliographic record

VenueJournal of Family Psychology · 2017
Typereview
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMentalizationPsychologySiblingPsycINFODevelopmental psychologyTheory of mindClinical psychologyCognitionPsychiatryMEDLINE

Abstract

fetched live from OpenAlex

The current study explored whether older sibling mentalization moderated the relationship between familial risk for internalizing symptoms and the development of future internalizing problems in the younger siblings, referred to as target children. Data were collected on 397 older siblings at Time 1 (T1) when target children were newborn and their older siblings were on average 2.61 years old (SD = .75). Target children were on average 1.60 years old at Time 2 (T2). Internalizing problems were assessed via mother and partner reports. Familial risk was operationalized as the average of all older siblings' level of internalizing problems. Older sibling mentalization, indexed by internal state talk and reasoning, was observed and coded during a sibling pretend-play interaction at T2. Results revealed a significant interaction between familial risk of internalizing problems and older siblings' mentalizing abilities, showing that familial risk was related to target children's internalizing problems in the absence of sibling mentalization. Familial risk was not associated with target children's internalizing problems when siblings demonstrated mentalizing abilities. Findings support the need to consider sibling mentalization as a protective factor for children's internalizing problems. (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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.149
GPT teacher head0.415
Teacher spread0.266 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2017
Admission routes2
Has abstractyes

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