Exploring the uptake and framing of research evidence on universal screening for intimate partner violence against women: a knowledge translation case study
Bibliographic record
Abstract
BACKGROUND: Significant emphasis is currently placed on the need to enhance health care decision-making with research-derived evidence. While much has been written on specific strategies to enable these "knowledge-to-action" processes, there is less empirical evidence regarding what happens when knowledge translation (KT) processes do not proceed as planned. The present paper provides a KT case study using the area of health care screening for intimate partner violence (IPV). METHODS: A modified citation analysis method was used, beginning with a comprehensive search (August 2009 to October 2012) to capture scholarly and grey literature, and news reports citing a specific randomized controlled trial published in a major medical journal on the effectiveness of screening women, in health care settings, for exposure to IPV. Results of the searches were extracted, coded and analysed using a multi-step mixed qualitative and quantitative content analysis process. RESULTS: The trial was cited in 147 citations from 112 different sources in journal articles, commentaries, books, and government and news reports. The trial also formed part of the evidence base for several national-level practice guidelines and policy statements. The most common interpretations of the trial were "no benefit of screening", "no harms of screening", or both. Variation existed in how these findings were represented, ranging from summaries of the findings, to privileging one outcome over others, and to critical qualifications, especially with regard to methodological rigour of the trial. Of note, interpretations were not always internally consistent, with the same evidence used in sometimes contradictory ways within the same source. CONCLUSIONS: Our findings provide empirical data on the malleability of "evidence" in knowledge translation processes, and its potential for multiple, often unanticipated, uses. They have implications for understanding how research evidence is used and interpreted in policy and practice, particularly in contested knowledge areas.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.263 | 0.402 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".