Exploring mothers’ experiences of trauma and violence-informed cognitive behavioural therapy following intimate partner violence: a qualitative case analysis
Bibliographic record
Abstract
PURPOSE: Intimate partner violence (IPV) is a pervasive public health issue impacting one in three women, worldwide. Broadly defined as any act of coercive control within the context of an intimate relationship, IPV often results in significant negative health outcomes. Cognitive Behavioral Therapy (CBT), a mainstay treatment for relational trauma, has particular relevance for women undergoing the many transformations associated with the perinatal period. The findings of this case analysis build upon existing literature supporting the positive impact of Trauma and Violence- Informed CBT (TVICBT) for women who have experienced IPV and are living with mental health challenges. METHODS: As part of a larger mixed-methods study, three women who experienced IPV and received TVICBT during pregnancy participated in in-depth, semi-structured interviews to determine the perceived value and acceptability of this intervention. FINDINGS: Insights gained herein, serve to enrich current evidence, suggesting that TVICBT provided during pregnancy may hold promise for the treatment of IPV-related mood and anxiety disorders. CONCLUSIONS: Given that improved perinatal mental health has been linked to enhanced maternal resiliency and quality of life, greater maternal-infant attachment, and positive child health outcomes, this research has the potential to bridge the critical knowledge gap at the intersection of motherhood, trauma, and mental health.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".