Emergency Knowledge Translation, COVID-19 and indoor air: evaluating a virtual ventilation and filtration consultation program for community spaces in Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background An October, 2021 review of Public Health Ontario's COVID-19 guidance for congregate settings such as shelters and long-term care homes demonstrated that this guidance did not include references to ventilation or filtration. In April 2022, an interdisciplinary team with expertise in indoor air quality (IAQ), engineering, epidemiology, community programming and knowledge translation launched a virtual ventilation and filtration consultation program for community spaces in Toronto, Ontario. The program gives people working in community spaces direct access to IAQ experts through 25-min online appointments. The program aims to help reduce the risk of COVID-19 transmission in community spaces, and was designed to help compensate for gaps in public health guidance and action. Methods Representatives from participating organizations (n. 27) received a link to an online survey via email in April 2023. Survey questions explored the impacts of the program on topics such as: purchase and use of portable air filters; maintenance and use of bathroom fans; and, maintenance and modification of HVAC systems. Survey participation was anonymous, and no demographic information was collected from participants. Results Representatives from 11 organizations completed the survey (40%). Of those who responded, nine (82%) made changes as a result of the program, with eight (73%) making two or more changes such as purchasing portable air filters and increasing routine maintenance of HVAC systems. Conclusions When presented with brief access to expert support and tailored plain language guidance, people working in community spaces increased their use of ventilation and filtration strategies for COVID-19 infection prevention and control.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.079 | 0.000 |
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 it