Attachment Video-feedback Intervention with Children and Their Foster Parents: A Case Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".