Strategies to promote uptake and use of intimate partner violence and child maltreatment knowledge: an integrative review
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) and child maltreatment (CM) are major social and public health problems. Knowledge translation (KT) of best available research evidence has been suggested as a strategy to improve the care of those exposed to violence, however research on how best to promote the uptake and use of IPV and CM evidence for policy and practice is limited. Our research asked: 1) What is the extent of IPV/CM-specific KT research? 2) What KT strategies effectively translate IPV/CM knowledge? and 3) What are the barriers and facilitators relevant to translating IPV/CM-specific knowledge? METHODS: We conducted an integrative review to summarize and synthesize the available evidence regarding IPV/CM-specific KT research. We employed multiple search methods, including database searches of Embase, CINAHL, ERIC, PsycInfo, Sociological Abstracts, and Medline (through April, 2013). Eligibility and quality assessments for each article were conducted by at least two team members. Included articles were analyzed quantitatively using descriptive statistics and qualitatively using descriptive content analysis. RESULTS: Of 1230 identified articles, 62 were included in the review, including 5 review articles. KT strategies were generally successful at improving various knowledge/attitude and behavioural/behavioural intention outcomes, but the heterogeneity among KT strategies, recipients, study designs and measured outcomes made it difficult to draw specific conclusions. Four key themes were identified: existing measurement tools and promising/effective KT strategies are underused, KT efforts are rarely linked to health-related outcomes for those exposed to violence, there is a lack of evidence regarding the long-term effectiveness of KT interventions, and authors' inferences about barriers, facilitators, and effective/ineffective KT strategies are often not supported by data. The emotional and sometimes contested nature of the knowledge appears to be an important barrier unique to IPV/CM KT. CONCLUSIONS: To direct future KT in this area, we present a guiding framework that highlights the need for implementers to use/adapt promising KT strategies that carefully consider contextual factors, including the fact that content in IPV/CM may be more difficult to engage with than other health topics. The framework also provides guidance regarding use of measurement tools and designs to more effectively evaluate and report on KT efforts.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".