A Mixed Methods Synthesis Investigating the Personal and Ecological Resources Promoting Mental Health and Resilience in Youth Exposed to Intimate Partner Violence
Bibliographic record
Abstract
Resilience research is concerned with studying the complex interplay of personal and ecological resources that promote positive adaptation following adversity in different populations. Although much research has investigated adjustment in young persons exposed to intimate partner violence (IPV), most of this research has taken a deficit approach by focusing on the negative cascades of effects that exposure to IPV has on the functioning of this group. In this paper, we discuss a mixed methods integration of two independent strength-based or resilience-focused studies involving Canadian youth exposed to IPV. Study 1 is a qualitative constructive grounded theory study that aimed to identify the coping strategies that youth exposed to IPV use to effectively cope with the traumatic experience of growing up in an IPV-affected family. This study included 13 youths with a history of IPV exposure who completed individual in-depth interviews, the drawing of ecomaps, and photovoice projects. Study 2 is a quantitative population-based study that aimed to identify profiles of adjustment in a cohort of 3886 youth who had previously experienced IPV exposure, as well as the specific risk and promotive factors that significantly predicted membership in the identified adjustment profiles. Both studies independently identified personal and ecological resources that were instrumental in supporting the resilience of study participants. By comparing and contrasting the two sets of findings, the present mixed methods integration provides further evidence on the complex interactions of mechanisms that promote positive adaptation in youth exposed to IPV, which aligns with a multisystemic understanding of resilience in this population. We provided recommendations for practice and policy based on the integrated findings.
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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 | 0.123 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".