Stories, Arts, and Equity: Intergenerational Qualitative Methods for Inclusive Public Health Research
Bibliographic record
Abstract
Health inequalities persist despite extensive public health research and interventions. Qualitative approaches are increasingly recognized for their ability to capture the nuanced, lived experiences of marginalized populations—an essential perspective in a world marked by complexity and uncertainty. This paper draws on two case studies to examine the role of qualitative methods in public health research, emphasizing the importance of amplifying marginalized voices and fostering intergenerational dialogue. Case Study 1 explores the use of visual methods to elevate the voices of people with dementia, a group often overlooked in traditional health research. Through video storytelling, participants shared their lived experiences, offering deeper insights into the challenges they face and the broader social implications of dementia. This case demonstrates the power of visual storytelling to capture the emotional and social dimensions of health inequalities while providing practical lessons for engaging vulnerable populations in research. Case Study 2 examines an intergenerational co-creation workshop series that used collaborative arts engagement to stimulate climate change dialogue amongst older adults (aged 60 years+) and children (aged 9 to 12). The project highlights the significance of creating spaces for cross-generational conversations and co-creative exploration, illustrating how such engagements can bridge knowledge gaps, address structural inequities such as ageism, and contribute to addressing climate-related health disparities. Together, the two case studies illuminate the potential of qualitative methods to advance health equity by supporting inclusive, relational, and justice-driven approaches to public health research. They underscore the value of participatory engagement in generating useful insights and fostering social change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.131 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".