Public engagement in health policymaking for older adults
Bibliographic record
Abstract
Public engagement has become a popular tool in modern policymaking, with its recognized benefits including developing better-informed policies, building trust, and tackling health inequities. Considering that older adults are one of the largest user groups of healthcare services and present diverse and complex health needs, public engagement could significantly benefit health policymaking for this demographic. However, despite the multitude of factors influencing the success of public engagement in realizing its intended benefits, limited research exists on the landscape and influencing factors of public engagement in this sector. This thesis aims to enhance our understanding of these elements. First, a scoping review of both peer-reviewed and grey literature is conducted to describe the characteristics and impacts of engagement initiatives in health policymaking for older adults. It explores the landscape of public engagement practices and provides recommendations to foster older adults’ contribution to health policymaking designed for them. Second, a comparative case study explains the interplay between political environments, public input gathered from engagement initiatives, and policy outcomes in two long-term care policymaking cases in Canada. Our findings reveal the interconnection between institutional factors, stakeholder interests, ideas, and external factors that shaped the policy outcomes, which did not incorporate the key input expressed through public engagement initiatives. Finally, an interpretive description study examines the perspectives of long-term care residents and their families in Ontario regarding legitimate representatives for their interests in long-term care policymaking. Participants identify core characteristics of intermediary agents deemed legitimate representatives, emphasizing the importance of mirroring the identity of, or having experience as, long-term care residents or family members. Taken together, these studies underscore the importance of a holistic approach to public engagement, incorporating thoughtful design elements and considering broader political contexts surrounding policymaking, in order for public engagement to deliver its intended benefits.
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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 | 0.046 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.006 |
| 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".