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Record W2126533882 · doi:10.1037/a0014070

Attachment-based intervention for maltreating families.

2008· article· en· W2126533882 on OpenAlexaff
George M. Tarabulsy, Katherine Pascuzzo, Ellen Moss, Diane St‐Laurent, Annie Bernier, Chantal Cyr, Karine Dubois‐Comtois

Bibliographic record

VenueAmerican Journal of Orthopsychiatry · 2008
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversité de MontréalUniversité du Québec à Trois-RivièresUniversité du Québec à MontréalUniversité Laval
Fundersnot available
KeywordsSocioemotional selectivity theoryIntervention (counseling)Developmental psychologyPsychologyContext (archaeology)Attachment theoryMaternal sensitivityStrange situationPsychiatry

Abstract

fetched live from OpenAlex

This article presents attachment theory-based intervention strategies as a means of addressing the core parent-child interaction deficits that characterize homes in which children are exposed to maltreatment. The article outlines the socioemotional and cognitive outcomes of maltreatment and proposes that although many prevention programs target different parental and family characteristics, few address the core relationship issues that are at stake. Recent research on attachment-based intervention strategies, aimed at improving the sensitivity and responsiveness of the parenting behaviors that children are exposed to, are presented as providing a means of addressing this domain. Attachment theory and research are briefly summarized, and the relational and interactional patterns observed in maltreating families, and their link to infant and child developmental outcome, are described. Research on attachment-based intervention is addressed, with a focus on studies conducted in the context of maltreating or high-risk families. This work is synthesized to present the basic components viewed as critical to effective attachment intervention with maltreating families. Finally, the authors end with recommendations aimed at the effective implementation of attachment-based intervention.

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 score0.103
Threshold uncertainty score0.533

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 head0.019
GPT teacher head0.371
Teacher spread0.352 · 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

Citations58
Published2008
Admission routes1
Has abstractyes

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