Knowledge Dissemination of Intimate Partner Violence Intervention Studies Measured Using Alternative Metrics: Results From a Scoping Review
Bibliographic record
Abstract
Alternative metrics measure the number of online mentions that an academic paper receives, including mentions in social media and online news outlets. It is important to monitor and measure dispersion of intimate partner violence (IPV) victim intervention research so that we can improve our knowledge translation and exchange (KTE) processes improving utilization of study findings. The objective of this study is to describe the dissemination of published IPV victim intervention studies and to explore which study characteristics are associated with a greater number of alternative metric mentions and conventional citations. As part of a larger scoping review, we conducted a literature search to identify IPV intervention studies. Outcomes included znumber of alternative metric mentions and conventional citations. Fifty-nine studies were included in this study. The median number of alternative metric mentions was six, and the median number of conventional citations was two. Forty-one percent of the studies (24/59) had no alternative metric mentions, and 27% (16/59) had no conventional citations. Longer time since publication was significantly associated with a greater number of mentions and citations, as were systematic reviews and randomized controlled trial designs. The majority of IPV studies receive little to no online attention or citations in academic journals, indicating a need for the field to focus on implementing strong knowledge dissemination plans. The papers receiving the most alternative metric mentions and conventional citations were also the more rigorous study designs, indicating a need to focus on study quality. We recommend using alternative metrics in conjunction with conventional metrics to evaluate the full dissemination of IPV research.
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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 | 0.311 | 0.646 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.072 | 0.071 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".