Themes of Healing and Posttraumatic Growth in Women Survivors’ Narratives of Intimate Partner Violence
Bibliographic record
Abstract
Research on the effects of intimate partner violence (IPV) on women demonstrates the significant physical, emotional, psychological, and spiritual consequences of this form of interpersonal trauma. It is well documented that experiencing IPV can have devastating consequences to women's physical and mental health, overall well-being, and quality of life, as well as that of their children's. However, a small, predominantly qualitative body of research exists on women's experience of and capacity for healing from the effects of IPV, but more research is needed to advance theory and practice in this important area. This study applied secondary analysis to an existing data set to answer the question, "What are the themes of healing and posttraumatic growth in ten diverse women's narratives of IPV?" Lengthy, detailed interview transcripts were rigorously subjected to inductive and deductive thematic analysis, which revealed three overarching themes, and six subthemes, of healing and posttraumatic growth in women's narratives: Awareness and Insight (subthemes: Discerning the Self and Understanding Relationships), Renewal and Reconstruction (subthemes: [Re]building the Self and Redefining Relationships), and Transformation and Meaning (subthemes: New Perspectives and Finding Purpose Through Helping Others). Findings further revealed that women's healing from the effects of IPV involves a multidimensional, personalized, nonlinear, and often transformative process that operates within themselves and through relationships. Practitioners working with women who have experienced IPV should consider survivors' potential for healing and target appropriate intervention strategies. Additional qualitative and longitudinal research with diverse populations would deepen understanding of the dynamics, variables, and circumstances that impact healing and posttraumatic growth for women exposed to IPV.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".