Women's Quality of Life After Leaving an Abusive Relationship: The Effects of Past and Ongoing Intimate Partner Violence, Mastery and Social Support
Bibliographic record
Abstract
Intimate Partner Violence (IPV) is considered a major stressor that threatens the lives of women globally. Although qualitative research suggests that IPV has substantial negative effects on women’s quality of life, few quantitative studies have examined the quality of life of women who have experienced IPV due, in part, to the lack of an appropriate, brief measure of QOL. Consequently, little is known about the contextual process by which IPV experiences affect women’s quality of life (QOL) after leaving an abusive relationship. Mastery and social support are two important resources that women may use to deal with IPV but whether they function as mediators of the relationships between recent and ongoing IPV experience and QOL is unknown. The purposes of this study were to: a) advance the measurement of both QOL and IPV by evaluating the psychometric properties of the QOL Scale and Index of Spouse Abuse scale (ISA) in a community sample of Canadian women; and, b) test a theoretical model that explains how women’s recent and ongoing experiences of IPV affect their QOL and whether social support and mastery mediate this process.\nA secondary analysis of data from a sample of 250 Canadian women who participated in Wave 5 of the Women’s Health Effects Study was conducted to address the study purposes. Support for the construct validity of both the Index of Spouse Abuse (ISA) and Quality of Life Scale was found using factor analysis techniques; evidence in support of the concurrent validity and internal consistency reliability of each scale was also found. Structural equation modeling (SEM) was used to examine whether social support and mastery mediate the relationship between the severity of recent and current IPV and women’s QOL. The proposed theoretical model was found to fit the data. Specifically, the severity of recent and ongoing IPV was found to affect women’s QOL directly and, indirectly, through mastery and social support, although the strength of the path coefficients differed.\nThe results demonstrate the reliability and validity of the ISA and QOL scales and contribute delineating the mediating effects of mastery and social support. The findings underscore the significance of considering recent and ongoing IPV experiences and women’s resources as key factors shaping QOL after separation from an abusive partner.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".