Narrative Therapy as an Intervention for Post-Divorce Adjustment and Grief: Examining Psychological Outcomes
Bibliographic record
Abstract
Objective: This study aimed to evaluate the effectiveness of narrative therapy in improving post-divorce adjustment and reducing grief symptoms among individuals experiencing marital dissolution. Methods and Materials: A quasi-experimental design was used with a treatment group (n = 36) receiving ten structured narrative therapy sessions and a control group (n = 36) receiving no intervention. Participants were assessed at three time points: pre-test, post-test, and follow-up. Standardized measures, including the Post-Divorce Adjustment Scale (PDAS) and the Grief Experience Questionnaire (GEQ), were administered to evaluate changes over time. Data were analyzed using repeated measures ANOVA and Bonferroni post-hoc tests to determine within-group and between-group differences in post-divorce adjustment and grief levels. Findings: Results showed a significant improvement in post-divorce adjustment in the treatment group compared to the control group (F = 57.90, p = 0.0001). Grief levels significantly decreased in the treatment group over time (F = 71.92, p = 0.00001), with Bonferroni post-hoc comparisons indicating that post-divorce adjustment increased significantly from pre-test to post-test (p = 0.001) and was maintained at follow-up (p = 0.0003), while grief decreased significantly from pre-test to post-test (p = 0.0001) and continued to decline at follow-up (p = 0.00001). Conclusion: The findings suggest that narrative therapy is an effective intervention for enhancing post-divorce adjustment and reducing grief. By enabling individuals to reconstruct their divorce narratives, the therapy promotes emotional healing, resilience, and future-oriented self-perceptions. Narrative therapy should be considered a valuable therapeutic approach for individuals struggling with the emotional consequences of divorce.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".