Examining the role of context in the implementation of a deliberative public participation experiment : Results from a Canadian comparative study.
Bibliographic record
Abstract
To resolve tensions among competing sources of evidence and public expectations, health-care managers and policy makers are turning more than ever to involve the public in a wide range of decisions. Yet efforts to use research evidence to inform public involvement decisions are hampered by an absence of rigorous public participation evaluation research. In particular, greater rigour in exploring the roles played by different contextual variables--such as characteristics of the issue of interest, the culture of the sponsoring organization and attributes of the decision being made--is needed. Using a comparative quasi-experimental design, we assessed the performance of a generic public participation method implemented in 5 Canadian regionalized health settings between 2001 and 2004. Participant and decision-maker perspectives were assessed and, through direct observation, the roles exerted by contextual variables over the public involvement processes were documented and analysed. Our findings demonstrate that a generic public participation method can be implemented in a variety of contexts and with considerable success. Context exerts fostering and inhibiting influences that contribute to more (or less) successful implementation. Public participation practitioners are encouraged to pay careful attention to the types of issues and decisions for which they are seeking public input. Sufficient organizational resources and commitment to the goals of the public participation process are also required. Attention to these contextual attributes and their influence on the design and outcomes of public participation processes is as important as choosing the "right" public participation mechanism.
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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 | 0.047 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".