Messy but worth it: human-centred design as applied within a successful vaccine-promotive campaign
Bibliographic record
Abstract
Human-centred design (HCD) is an approach to problem-solving that prioritises understanding and meeting the needs of the end-users. Researchers and designers practice empathic listening as users share their perspectives, thereby enabling a variety of stakeholders to cocreate effective solutions. While a valuable and, in theory, straightforward process, HCD in practice can be chaotic: Practitioners often struggle to navigate an excess of (often conflicting) ideas and to strike a balance between problem-understanding and problem-solving. In this practice paper, we outline our own experiences with HCD, which ultimately resulted in the development of a successful video-based intervention to bolster vaccine confidence in the Philippines. We highlight the use of 'radical circles' to overcome roadblocks and navigate tensions. Radical circles entail groups of individuals with divergent opinions and identities engaging in critical analysis of a given idea, actively challenging standard ways of thinking, and ultimately, generating solutions. Employing radical circles enabled us to innovate and adapt to new perspectives that emerged along the non-linear HCD pathway. Our incorporation of radical circles into HCD methodology demonstrates its potential as a powerful complementary step in the meaning-making process. In our view, radical circles could enrich HCD processes and provide a solution to design overcrowding, leading to meaningful, transformative and successful interventions.
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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 | 0.069 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".