Moving knowledge about family violence into public health policy and practice: a mixed method study of a deliberative dialogue
Bibliographic record
Abstract
BACKGROUND: There is a need to understand scientific evidence in light of the context within which it will be used. Deliberative dialogues are a promising strategy that can be used to meet this evidence interpretation challenge. METHODS: We evaluated a deliberative dialogue held by a transnational violence prevention network. The deliberative dialogue included researchers and knowledge user partners of the Preventing Violence Across the Lifespan (PreVAiL) Research Network and was incorporated into a biennial full-team meeting. The dialogue included pre- and post-meeting activities, as well as deliberations embedded within the meeting agenda. The deliberations included a preparatory plenary session, small group sessions and a synthesizing plenary. The challenge addressed through the process was how to mobilize research to orient health and social service systems to prevent family violence and its consequences. The deliberations focused on the challenge, potential solutions for addressing it and implementation factors. Using a mixed-methods approach, data were collected via questionnaires, meeting minutes, dialogue documents and follow-up telephone interviews. RESULTS: Forty-four individuals (all known to each other and from diverse professional roles, settings and countries) participated in the deliberative dialogue. Ten of the 12 features of the deliberative dialogue were rated favourably by all respondents. The mean behavioural intention score was 5.7 on a scale from 1 (strongly disagree) to 7 (strongly agree), suggesting that many participants intended to use what they learned in their future decision-making. Interviews provided further insight into what might be done to facilitate the use of research in the violence prevention arena. CONCLUSION: Findings suggest that participants will use dialogue learnings to influence practice and policy change. Deliberative dialogues may be a viable strategy for collaborative sensemaking of research related to family violence prevention, and other public health topics.
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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 | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".