Dyadic attachment-based therapies for infants and young children with mental health problems: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Early child-caregiver attachment is foundational to mental health (MH). While prevention efforts often aim to improve attachment quality, clinicians frequently encounter infants and young children already exhibiting clinical symptoms of MH disorders. A comprehensive summary of attachment-based dyadic interventions for this population is lacking. This scoping review aims to address this gap. METHODS: We conducted a scoping review of CINAHL, MEDLINE, PsycINFO, Web of Science, Cochrane CENTRAL and hand-searched articles to identify and characterize dyadic, relationship-based interventions for children aged 0-6 years with clinical symptoms of MH disorders. Studies were screened for eligibility and included if they examined therapeutic modalities used in clinical populations beyond preventive approaches. RESULTS: Screening identified studies that evaluated several therapeutic modalities, e.g., Parent Child Interaction Therapy (PCIT), Early Pathways (EP), Watch, Wait, and Wonder, Parent-Infant Psychotherapy, and Video Feedback Interventions. PCIT and EP had the most published data, treated the largest number of participants, and demonstrated significant improvements in child or relational outcomes. However, most studies had small sample sizes and methodological limitations. Only a few interventions had been evaluated using rigorous designs such as randomized controlled trials. CONCLUSIONS: Two interventions that had the most evidence were EP and PCIT, particularly for families affected by adverse social determinants of health. Both require further research to explore barriers for implementation (e.g., adaptability in multiple settings and cultures, lessen resources required for service delivery, etc.). Additional research is needed to strengthen the evidence base for dyadic, attachment-based treatments targeting clinical MH concerns in infants and young children.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".