Scale, Scope, Speed: Reflections on a Multi-site Covid-19 Study
Bibliographic record
Abstract
Designers have a unique role to play in public health, but their involvement requires an examination their practices and methods for their fit with this new context. This article reflects on the experiences of a multi-site design team collaborating across the US and Canada to explore early-stage Covid-19 patient recovery experiences. A unique feature of this project is that it was conceived of, led by, and executed by designers situated in health systems and health research units working in diverse geographies to jointly investigate a public health phenomenon at a broad scale. We discuss three challenges to design practice encountered in this context—scale, scope, and speed. Lastly, we draw from the design teams’ cross-sector expertise to pose key questions for design as it migrates to the public health sector. • Design’s value to healthcare and public health is in increasingly demand. • Public health initiatives bring changes in scale, scope and speed for which design practices may not yet be optimized. • Design for public health research offers an emerging domain for new design knowledge. • Adapting the design skill set and toolset to fit public health is a frontier of practice.
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 | 0.170 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.041 | 0.039 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.008 | 0.031 |
| Research integrity | 0.007 | 0.015 |
| 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".