Priorities for research in child maltreatment, intimate partner violence and resilience to violence exposures: results of an international Delphi consensus development process
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) and child maltreatment (CM) are major global public health problems. The Preventing Violence Across the Lifespan (PreVAiL) Research Network, an international group of over 60 researchers and national and international knowledge-user partners in CM and IPV, sought to identify evidence-based research priorities in IPV and CM, with a focus on resilience, using a modified Delphi consensus development process. METHODS: Review of existing empirical evidence, PreVAiL documents and team discussion identified a starting list of 20 priorities in the following categories: resilience to violence exposure (RES), CM, and IPV, as well as priorities that cross-cut the content areas (CC), and others specific to research methodologies (RM) in violence research. PreVAiL members (N = 47) completed two online survey rounds, and one round of discussions via three teleconference calls to rate, rank and refine research priorities. RESULTS: Research priorities were: to examine key elements of promising or successful programmes in RES/CM/IPV to build intervention pilot work; CC: to integrate violence questions into national and international surveys, and RM: to investigate methods for collecting and collating datasets to link data and to conduct pooled, meta and sub-group analyses to identify promising interventions for particular groups. CONCLUSIONS: These evidence-based research priorities, developed by an international team of violence, gender and mental health researchers and knowledge-user partners, are of relevance for prevention and resilience-oriented research in the areas of IPV and CM.
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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 | 0.351 | 0.305 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".