Public Engagement in Health Policy‐Making for Older Adults: A Systematic Search and Scoping Review
Bibliographic record
Abstract
INTRODUCTION: As the world's population ages, there has been increasing attention to developing health policies to support older adults. Engaging older adults in policy-making is one way to ensure that policy decisions align with their needs and priorities. However, ageist stereotypes often underestimate older adults' ability to participate in such initiatives. This scoping review aims to describe the characteristics and impacts of public engagement initiatives designed to help inform health policy-making for older adults. METHODS: A systematic search of peer-reviewed and grey literature (English only) describing public engagement initiatives in health policy-making for older adults was conducted using six electronic databases, Google and the Participedia website. No geographical, methodological or time restrictions were applied to the search. Eligibility criteria were purposefully broad to capture a wide array of relevant engagement initiatives. The outcomes of interest included participants, engagement methods and reported impacts. RESULTS: This review included 38 papers. The majority of public engagement initiatives were funded or initiated by governments or government agencies as a formal activity to address policy issues, compared to initiatives without a clear link to a specific policy-making process (e.g., research projects). While most initiatives engaged older adults as target participants, there was limited reporting on efforts to achieve participant diversity. Consultation-type engagement activities were most prevalent, compared to deliberative and collaborative approaches. Impacts of public engagement were frequently reported without formal evaluations. Notably, a few articles reported negative impacts of such initiatives. CONCLUSION: This review describes how public engagement practices have been conducted to help inform health policy-making for older adults and the documented impacts. The findings can assist policymakers, government staff, researchers and seniors' advocates in supporting the design and execution of public engagement initiatives in this policy sector. PATIENT OR PUBLIC CONTRIBUTION: Older adult partners from the McMaster University Collaborative for Health and Aging provided strategic advice throughout the key phases of this review, including developing a review protocol, data charting and synthesis and interpreting and presenting the review findings. This collaborative partnership was an essential aspect of this review, enhancing its relevance and meaningfulness for older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".