The Effectiveness of Narrative Therapy With Children's Social and Emotional Skill Development: An Empirical Study of 813 Problem-Solving Stories
Bibliographic record
Abstract
This article examines the effectiveness of narrative therapy in boosting 8- to 10-year-old children's social and emotional skills in school. Data were collected from 353 children over two years, and two research assistants independently coded 813 stories. Children's personal accounts of their attempts at solving conflicts in their daily lives were collected before and after a series of narrative conversations, and compared to stories collected during the same time interval with a control group. The control data included a set of stories from waitlisted participants and those from students assigned to only a control group. The results of the study show that children receiving narrative therapy intervention showed a significant improvement in self-awareness, self-management, social awareness/empathy, and responsible decision making when compared to their own first stories and the stories from children in the control group. Improvement in relationship skills was present in both cohorts but was significant only for the second year. There was no significant gender difference. Narrative therapy practices such as externalizing and re-authoring can significantly contribute to the development of children's social and emotional skills. Implications of these results are discussed for all forms of therapeutic interventions, regardless of theoretical orientation.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".