DELIBERATIVE DIALOGUES: OPPORTUNITIES FOR BRIDGING GERONTOLOGICAL RESEARCH WITH POLICY AND PRACTICE
Bibliographic record
Abstract
Deliberative dialogues is a methodology that provides an integrated framework for concurrently generating and analyzing data, engaging participants, and synthesizing evidence. This methodology offers an important opportunity to bridge gerontological research with policy and practice and can lead to community investment and asset sharing by integrating the knowledge and experiences of multiple stakeholder groups. This symposium will present work from researchers representing the Behavioral and Social Sciences and Social Research, Policy, and Practice Research sections. The papers presented are based on a collection of research projects in Western Canada that explore different aspects of service and housing provision for seniors. Battersby et al. present World Café workshop findings from dialogues with housing providers who have had experience with mass interinstitutional relocations in long-term care. Fang et al. report on Perspectives Workshops with service providers that resulted in findings which informed services and programs offered to tenants of a low-income seniors’ rental property. Canham et al. discuss methods of engagement with Housing First seniors’ service providers during a Mapping Workshop and will report findings of available Housing First resources and where there are service gaps. Finally, Wada et al. highlight findings from an interactive research engagement with knowledge users and research participants at the end of a two-year evaluation project during a Research Day. To conclude this symposium, our discussant will summarize the papers using an interdisciplinary perspective and will discuss the implications of using innovative methods in bridging policy and practice.
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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 | 0.321 | 0.207 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.025 | 0.118 |
| Scholarly communication | 0.043 | 0.054 |
| Open science | 0.009 | 0.058 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".