IMPAACT: IMproving the PArticipAtion of older people in policy decision-making on common health CondiTions – a study protocol
Bibliographic record
Abstract
INTRODUCTION: Rapid population ageing is a demographic trend being experienced and documented worldwide. While increased health screening and assessment may help mitigate the burden of illness in older people, issues such as misdiagnosis may affect access to interventions. This study aims to elicit the values and preferences of evidence-informed older people living in the community on early screening for common health conditions (cardiovascular disease, diabetes, dementia and frailty). The study will proceed in three Phases: (1) generating recommendations of older people through a series of Citizens' Juries; (2) obtaining feedback from a diverse range of stakeholder groups on the jury findings; and (3) co-designing a set of Knowledge Translation resources to facilitate implementation into research, policy and practice. Conditions were chosen to reflect common health conditions characterised by increasing prevalence with age, but which have been underexamined through a Citizens' Jury methodology. METHODS AND ANALYSIS: This study will be conducted in three Phases-(1) Citizens' Juries, (2) Policy Roundtables and (3) Production of Knowledge Translation resources. First, older people aged 50+ (n=80), including those from traditionally hard-to-reach and diverse groups, will be purposively recruited to four Citizen Juries. Second, representatives from a range of key stakeholder groups, including consumers and carers, health and aged care policymakers, general practitioners, practice nurses, geriatricians, allied health practitioners, pharmaceutical companies, private health insurers and community and aged care providers (n=40) will be purposively recruited for two Policy Roundtables. Finally, two researchers and six purposively recruited consumers will co-design Knowledge Translation resources. Thematic analysis will be performed on documentation and transcripts. ETHICS AND DISSEMINATION: Ethical approval has been obtained through the Torrens University Human Research Ethics Committee. Participants will give written informed consent. Findings will be disseminated through development of a policy brief and lay summary, peer-reviewed publications, conference presentations and seminars.
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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 | 0.088 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.089 | 0.019 |
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".