Evaluating Public Engagement for a Consensus Development Conference
Bibliographic record
Abstract
BACKGROUND: Effectively engaging Canadians to help improve the quality and delivery of healthcare to dying Canadians is a priority for healthcare administrators and policy makers. This report shares our evaluation and learnings, applying a series of strategies to encourage policy formation. The Palliative Care Matters consensus development conference held in Ottawa on November 7-9, 2016 brought together members of the public, stakeholders, scientific experts, and a lay panel of interested Canadians to examine Canadian public opinions on palliative care and question experts on how palliative care could be enhanced. OBJECTIVE: This report shares our evaluation and learnings applying a series of strategies to encourage policy formation. METHODS: An evaluation was conducted to measure the short, intermediate, and identify long-term outcomes of the conference. The overall performance of the conference for public engagement from November 2016 to mid June 2017 is shared. RESULTS AND CONCLUSION: The outcome of the conference was positive. It was attended and watched online by over 400 participants, received national print, radio and television coverage, and generated high exposure and engagement on social media. Survey results showed that the majority of steering committee, expert, and lay panel members felt a high level of engagement and agreed that the engagement process was successful. Evaluation will be conducted on an ongoing basis for at least another year.
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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 | 0.576 | 0.578 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".