Take These Broken Wings and Learn to Fly: Applying Resilience Concepts to Practice with Children and Youth Exposed to Intimate Partner Violence
Bibliographic record
Abstract
A credible body of research has evolved on resilience and children exposed to intimate partner violence (IPV). This information can be drawn on for resilience-informed approaches specifically aimed at working with children exposed to IPV. Child exposure to IPV has been an area of growing interest with rates in both child welfare and community samples remaining at concerning levels. It is commonly accepted that a number of these children experience harmful effects. However, extant studies also indicate some children show resilience after IPV exposure. Yet little has been written on how resilience can be fostered with exposed children who are negatively affected. The authors offer a working definition of it, discuss related concepts, and summarize the resilience research regarding IPV-exposed children. As well, two case examples are presented for ways to foster resilience with IPV-exposed children. Suggestions are made for a resilience-informed approach with this population, and it is demonstrated how social workers can use this to reinforce a strengths-based framework. Suggestions for future research and practice are also made.
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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".