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Record W7105864058 · doi:10.1080/13575279.2025.2585042

Attachment Video-feedback Intervention with Children and Their Foster Parents: A Case Study

2025· article· en· W7105864058 on OpenAlexaff

Bibliographic record

VenueChild Care in Practice · 2025
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsIntervention (counseling)Foster careQualitative researchFoster parentsChild care

Abstract

fetched live from OpenAlex

The Attachment Video-feedback Intervention (AVI), grounded in attachment theory, employs video feedback to enhance parental behaviors, particularly parental sensitivity, with the goal of optimizing child development and improving attachment security. The AVI program has been evaluated with vulnerable children and at-risk parents, demonstrating significant positive effects for both parents and children. This paper outlines the design of the AVI program and details its implementation in this specific intervention with foster parents and young children in France. A detailed case study is presented to illustrate the application and effectiveness of the program within this specific population, providing valuable insights for clinicians and practitioners. Throughout the AVI program, which included eight intervention sessions with video feedback, improvements were observed in both positive parenting practices and child development. Specifically, we observed an improvement in the foster mother’s reflective functioning and an increased sense of self-efficacy, alongside a significant reduction in the foster child’s internalizing and externalizing behaviors. By detailing the intervention process, our aim is to illustrate the mechanisms at work within the AVI and to contribute to practitioners’ reflections on attachment-based approaches to supporting foster parenting.

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 score0.364
Threshold uncertainty score0.597

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 head0.013
GPT teacher head0.385
Teacher spread0.372 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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