The longitudinal interplay between insecure attachment behaviors and psychosocial strengths among children in child welfare services
Bibliographic record
Abstract
Abstract Children who have experienced maltreatment are more likely to have disrupted attachments, fewer psychosocial strengths, and poorer long-term psychosocial outcomes. However, few studies have examined the interplay between attachment security and psychosocial strengths among children involved in therapeutic services in the context of the child welfare system. The present longitudinal study examines the insecure attachment behaviors and psychosocial strengths of 555 children referred to the Therapeutic Family Care program (TFCP) in Cobourg, Ontario between 2000 and 2019. The children were assessed by their caregivers on a regular basis using the Assessment Checklist for Children (ACC) and the complementary strengths-focused ACC+ measure. Average age of children at baseline was 9.57 years (SD = 3.51) and 229 (41.26%) were female. We conducted growth curve and random intercepts cross-lagged panel models to test the longitudinal interplay between insecure attachment behaviors and strengths. Results suggest that females’ attachment security improved, males’ attachment security worsened, and both males and females developed strengths over time. Further, analyses revealed a directional effect, whereby fewer insecure attachment behaviors predicted more psychosocial strengths approximately 6 months later. Implications for attachment-oriented and strengths-based services in the context of child welfare are 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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".