Critical Interventions, Real Conversations: Discursive Design for Culturally Tailored Smoking Cessation
Bibliographic record
Abstract
This exploratory study examines how discursive design—using provocative, speculative artifacts to spark reflection and discussion—might expand public health experts’ problematization of approaches to tailoring and targeting interventions. Cultural tailoring and targeting (CTT) refers to adapting interventions for specific sociocultural populations. Because LGBTQ+ communities experience disproportionately high rates of tobacco use, this study applies discursive intervention concepts within this context to explore how they might help experts critically engage with CTT strategies for reaching LGBTQ+ populations more effectively. To investigate this, two pairs of discursive intervention concepts were designed and presented to three focus groups of public health experts. Each pair juxtaposed a conventional intervention approach with a more provocative, unfamiliar one—for example, deepfake-driven behavior disruption. The goal was to document the type of conversation discursive design could stimulate around CTT considerations and generate insights relevant to the value of design methodologies to foster new ways to problematize public health matters. Findings indicate that the concepts prompted critical conversations about CTT, although the depth and focus of engagement varied. Those with greater expertise in LGBTQ+ issues engaged more with CTT mechanisms and implications, while others focused on implementation and feasibility concerns—essential to intervention development but outside the study’s focus. These patterns highlight who should be included in such efforts and how they should be engaged from a facilitation perspective, raising important considerations for methodological refinements and future research. Overall, this initial exploration aims to uncover the potential of discursive design to deepen understanding of CTT interventions and inform more responsive, innovative approaches to addressing tobacco use among priority populations.
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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 | 0.065 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".