Communication tools to support public understanding and awareness of COVID-19 information
Bibliographic record
Abstract
Building evidence-based knowledge, and access to the right information at the right time, are critical factors in enhancing health and wellbeing within communities, particularly during a health crisis such as a pandemic. The COVID-19 pandemic required trusted information resources and effective communication tools to support public understanding and awareness of COVID-19 information. The COVID-19 Printables project was a collaborative initiative which aimed to design and develop a rapidly deployable and inclusive communication tool to inform diverse communities and populations about COVID-19 precautions and response. The Printables were initiated to fill a public health communication gap in understandable and accessible communication tools for lower literacy levels, and minority and marginalized groups, such as immigrant and refugee communities. A community based participatory approach supported the engagement of community members and frontline physicians in the design process, guided by health information behaviour and social inclusion frameworks. The project resulted in the development of a series of open access, easy to use, adaptable, and multilingual (40+ languages) printables that have been used widely from emergency departments to refugee services and community health centres, in Canada and worldwide. They have been used by over 40,000 people in Canada alone.
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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".