Public engagement can change your research, but how can it change your research institution? ICES Case Study
Bibliographic record
Abstract
This article explores the approach that ICES (formerly the Institute for Clinical Evaluative Sciences) uses to encourage public engagement at both the research study and corporate level. ICES is an independent not-for-profit research institute in the province of Ontario, Canada. This article was co-written by ICES' public engagement team and four members of the ICES Public Advisory Council (PAC). As part of the process of writing this article PAC members provided their reflections on why they got involved, what worked well and the limitations and challenges of ICES' approach. ICES described the development of its public engagement strategy to inform how the institution would capture and incorporate the values of Ontarians in ICES activities and research. ICES provided details on two key elements of its strategy: the formation of a PAC to advise its leadership, and the creation of resources and supports to encourage researchers to incorporate public engagement in their projects. PAC members and ICES provided perspectives on what impact they perceive as a result of the public engagement strategy. PAC members expressed that ICES has demonstrated listening to and using their input, but it is too early to evaluate if their feedback has changed the way ICES conducts its work. ICES discussed the challenges and successes in building and implementing the public engagement strategy, including recruiting a diverse council, aligning with public priorities and creating a culture of engagement. As a result of public input, ICES has restructured the way the institution explains its privacy and cybersecurity approach to build trust and confidence. ICES has also seen an increase in researchers using public engagement resources, and early data suggests that in 2019 about 20% of scientists included some form of public engagement in their projects. ICES' journey to public engagement resulted in important changes to processes and activities at the institution, but there is much more that needs to be done. PAC members advocate that public members should be engaged in health data research and hope that public input will be a core element in health data research in the future. ICES will continue its efforts to address public priorities and will seek to further evaluate the impact of public engagement across the organisation.
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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 | 0.050 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.035 | 0.022 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".