Combining the World Café and the Deliberative Democratic Evaluation: A Win-Win Strategy
Bibliographic record
Abstract
The current emergency response for substance users experiencing homelessness is not adapted to their needs. This has led to the revolving door phenomenon and to issues of collaboration between involved organizations. This study aims to demonstrate the relevance from the participants’ perspective of: 1) the World Café; 2) the deliberative democratic evaluation; and 3) combining these two methods as part of a qualitative study aimed at improving emergency response to substance users experiencing homelessness in Montreal. Thirty-four people participated in an intersectoral World Café guided by the principles of deliberative democratic evaluation. Twenty-three participants responded to a questionnaire regarding their satisfaction, effects of their participation, and adherence to the principles of deliberative democratic evaluation (inclusion, dialogue, and deliberation). We performed descriptive statistics and a thematic content analysis. Ultimately, the respondents were satisfied with the activity and several said they gained new knowledge and improved their network. Roughly half of the participants shared that their participation influenced their representations and their future practices. Our findings suggest that combining deliberative democratic evaluation and World Café is relevant to providing rich qualitative data, gaining systemic insight, and having an impact on our communities (e.g., improving intersectoral collaboration, professional’ attitudes and practices).
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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 | 0.197 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.037 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".