Championing Older Adults in Climate Empowerment (COALESCE) Phase 1 - A Participatory Rapid Realist Review
Bibliographic record
Abstract
Abstract Background The COALESCE Project (Phase I) investigates how older adults experience and prioritise the effects of climate breakdown. By engaging them as citizen science co-researchers, the project aims to co-develop resilience strategies tailored to their needs. Partnering older adults with graduate students fostered intergenerational exchange, enhancing research capacity and generating practical interventions to address severe weather impacts on ageing populations. Methods A participant-led rapid realist review (PRRR) was piloted to explore how best to identify climate change priorities and effective interventions. Five graduate students and seven older adult co-researchers were trained to collaboratively undertake a time-limited PRRR, including literature search, data extraction, and synthesis. Student–older adult pairs conducted targeted searches of academic and grey literature on climate-related impacts and resilience strategies for older people. Guided by older adults’ lived experiences, the review process integrated life-course interviews and deliberative dialogues, enabling reflection, critique, and adaptation of the PRRR method. Findings The pilot showed that effective co-research requires working to the unique strengths of both older adults and students. Rather than duplicating tasks, collaboration was optimised when students applied academic research skills alongside older adults’ lived experience and contextual knowledge, ensuring shared responsibility and ownership of the review. Discussion This intergenerational co-research model deepened understanding of climate impacts on older people, built mutual research capacity, and empowered participants. Critically, the project demonstrated the potential of participatory realist reviews to co-produce knowledge and inform interventions, ensuring older adults define their own climate change contexts and resilience needs.
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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 | 0.311 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".