The Art of Early Life: A Mixed-Methods Evaluation of Arts-based Approaches to Translating the Developmental Origins of Health and Disease Science for Public Health Impact
Bibliographic record
Abstract
Background: Public understanding of the Developmental Origins of Health and Disease (DOHaD) remains limited, hindering efforts to shift health behaviours and policy. Arts-based knowledge translation (ABKT) offers a promising approach to engage the public with complex health science. Methods: We evaluated the feasibility, acceptability, and impact of a multi-site public exhibition co-developed by scientists, artists, and community members to convey DOHaD concepts. The exhibition, shown at five sites in a mid-sized Canadian city, featured professional and community-created artworks, scientific images, and interactive data collection activities. Using a parallel convergent mixed methods design, we collected and analyzed observational field notes, written reflections, QR code analytics, and structured activity responses across settings. Quantitative data were analyzed using logistic regression and descriptive statistics. Qualitative data underwent conventional content analysis. Findings were integrated narratively. Results: The exhibition was successfully installed across all sites, although space and security constraints in community settings required substantial adaptation. 89% of observed individuals at the gallery site engaged with the exhibition compared to 3-17% of observed individuals at community sites. Attendees at the gallery site also generally engaged with more artworks and for longer periods of time than at community sites. Viewing over half of the artworks was associated with a significantly greater odds of endorsing environmental and structural determinants of health (OR=10.3, 95% CI [1.48, 210.7]). Written and verbal feedback demonstrated increased awareness, emotional engagement, and a desire for further information. Despite some misinterpretation and low fidelity in interactive activities, the exhibition was broadly well-received and evoked critical reflection. Conclusions: This study provides the first empirical evaluation of an arts-based strategy to translate DOHaD concepts for public audiences, demonstrating that such approaches can enhance health literacy, foster dialogue about the social determinants of health, and support equitable public health communication about early life influences on lifelong health. Future efforts should optimize engagement in non-gallery spaces and assess longer-term impacts on attitudes and behaviours.
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 | 0.108 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".