REPLAY: It’s March 11th. Let’s try to fight Covid differently. How would you do it?
Bibliographic record
Abstract
Suppose you have the ability to go back to March 11th of 2020 - the date the WHO officially declared COVID-19 a pandemic1. What could have been done differently with respect to digital health product development? Were domain experts utilized to the best of their abilities? What can we learn from COVID-19 for future public health crises planning? Digital health data is now, more than ever, deeply influencing our lives. Having robust digital health tools to support public health surveillance is no longer a problem for technocrats. It’s everyone’s business now. Through the COVID-19 pandemic, I had the opportunity to lead a digital health startup, Flatten.ca, that focused on collecting symptom data from people in Canada and in Somalia2. This experience led to collaborations with big-tech companies, researchers, government officials, startups, and investors. Reflecting back on my experiences, I see many flaws in the process by which the North-American community decided to develop and deploy digital health tools. Simply put, a lack of speed and strategically allocated domain expertise hindered our success. Why did Canada take 5 months to launch a contact tracing app and Singapore only 10 days? This perspective piece aims at answering the questions highlighted above and analyzing the North-American response to build digital health tools as the COVID-19 pandemic rages on. Whether you’re an investor, academic, student, founder, or otherwise involved in the digital health industry, and you’re thinking about how to effectively support product development for future public health crises, this can help better inform where your efforts are best spent.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.092 | 0.049 |
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".