Effect of Attachment and Child Health (ATTACHTM) Parenting Program on Parent-Infant Attachment, Parental Reflective Function, and Parental Depression
Bibliographic record
Abstract
High-risk families exposed to toxic stressors such as family violence, depression, addiction, and poverty, have shown greater difficulty in parenting young children. In this study, we examined the effectiveness of ATTACHTM, a 10−12 session manualized one-on-one parental Reflective Function (RF)-based parenting program designed for high-risk families. Outcomes of parent-child attachment and parental RF were assessed via the Strange Situation Procedure (SSP) and Reflective Function Scale (RFS), respectively. The protective role of ATTACHTM on parental depression was also assessed. Data were available from caregivers and their children < 6 years of age who participated in five pilot randomized control trials (RCTs) and quasi-experimental studies (QES; n = 40). Compared with the control group, caregivers who received the ATTACHTM-program demonstrated a greater likelihood of secure attachment with their children (p = 0.004) and higher parental RF [self (p = 0.004), child (p = 0.001), overall (p = 0.002)] in RCTs. A significant improvement in parental RF (p = 0.000) was also observed in the QES within ATTACHTM group analysis. As attachment security increased, receiving the ATTACHTM program may be protective for depressed caregivers. Results demonstrated the promise of ATTACHTM for high-risk parents and their 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".