Play completion predicts fewer child psychological difficulties: A longitudinal study of mentalizing processes.
Bibliographic record
Abstract
Play therapy is widely used with children, including children who experienced sexual abuse. This longitudinal study examined whether more pretend play completion (PPC) at Time 1 predicted fewer child difficulties when this was assessed 3 years later at Time 2. Participants were 91 children (aged 3–8 at Time 1), including 51 children who experienced sexual abuse (child sexual abuse [CSA]). Play was coded with the Children’s Play Therapy Instrument, and child psychological difficulties were assessed with the Child Behavior Checklist. More PPC at Time 1 predicted fewer child psychological difficulties 3 years later at Time 2. This was the case in children who had experienced CSA as well as the comparison group, showing that PPC is predicts better psychological adjustment in both abused and nonabused children. The study provides the first longitudinal evidence of the important role of pretend play narrative completion in predicting less internalizing and externalizing difficulties. The findings have important clinical implications for play therapists. It suggests that interventions that encourage children to elaborate and complete their play narratives could facilitate agency and psychological adjustment, as well as recovery after trauma. This is in line with the idea that through play children discover that they can “play with reality” and gain control over how they tell their stories. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".