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Record W1998910858 · doi:10.1080/14616734.2015.1006383

An attachment-based intervention for parents of adolescents at risk: mechanisms of change

2015· article· en· W1998910858 on OpenAlexafffund
Marlene M. Moretti, Ingrid Obsuth, Stephanie G. Craig, Tania Bartolo

Bibliographic record

VenueAttachment & Human Development · 2015
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsSimon Fraser University
FundersCanadian Institutes of Health Research
KeywordsPsychologyIntervention (counseling)Developmental psychologyAnxietyClinical psychologyAffect (linguistics)Psychiatry

Abstract

fetched live from OpenAlex

Mechanisms that account for treatment effects are poorly understood. The current study examined processes that may underlie treatment outcomes of an attachment-based intervention (Connect) for parents of pre-teens and teens with serious behavior problems. Parents (N = 540) in a non-randomized trial reported on their teen's functioning prior to and following treatment. Results confirmed significant decreases in parents' reports of teens' externalizing and internalizing symptoms, replicating prior evaluations of this program. Reductions in parents' reports of teen attachment avoidance were associated with decreases in externalizing symptoms, while reductions in parents' reports of teen attachment anxiety were associated with decreases in internalizing symptoms. Parents' reports of improved teen affect regulation were also associated with decreases in both internalizing and externalizing symptoms. Results were comparable across gender and for parents of teens with pre-treatment externalizing symptoms in the clinical versus sub-clinical range. A model of therapeutic change in attachment-based parenting programs is discussed.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.101
GPT teacher head0.371
Teacher spread0.270 · 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

Citations97
Published2015
Admission routes2
Has abstractyes

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