Evaluating deliberative dialogues focussed on healthy public policy
Bibliographic record
Abstract
BACKGROUND: Deliberative dialogues have recently captured attention in the public health policy arena because they have the potential to address several key factors that influence the use of research evidence in policymaking. We conducted an evaluation of three deliberative dialogues convened in Canada by the National Collaborating Centre for Healthy Public Policy in order to learn more about deliberative dialogues focussed on healthy public policy. METHODS: The evaluation included a formative assessment of participants' views about and experiences with ten key design features of the dialogues, and a summative assessment of participants' intention to use research evidence of the type that was discussed at the dialogue. We surveyed participants immediately after each dialogue was completed and again six months later. We analyzed the ratings using descriptive statistics and the written comments by conducting a thematic analysis. RESULTS: A total of 31 individuals participated in the three deliberative dialogues that we evaluated. The response rate was 94% (N = 29; policymakers (n = 9), stakeholders (n = 18), researchers (n = 2)) for the initial survey and 56% (n = 14) for the follow-up. All 10 of the design features that we examined as part of the formative evaluation were rated favourably by all participant groups. The findings of the summative evaluation demonstrated a mean behavioural intention score of 5.8 on a scale from 1 (strongly disagree) to 7 (strongly agree). CONCLUSION: Our findings reinforce the promise of deliberative dialogues as a strategy for supporting evidence-informed public health policies. Additional work is needed to understand more about which design elements work in which situations and for different issues, and whether intention to use research evidence is a suitable substitute for measuring actual behaviour change.
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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 | 0.241 | 0.438 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| 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; 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".