Barriers and opportunities faced by public health practitioners in using public health guidance on COVID-19: a knowledge translation exercise for the eCOVID-19 RecMap
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The COVID-19 pandemic prompted the scientific community to collaborate in an unprecedented way, with the rapid and urgent generation and translation of new knowledge about the disease and its causative agent. Iteratively, and at different levels of government and globally, population-level guidance was created and updated, resulting in the need for a living catalog of guidelines, the eCOVID-19 Recommendations Map and Gateway to Contextualization (RecMap). This article focuses on the approach that was used to analyze barriers and opportunities associated with using the RecMap in public health in Canada. STUDY DESIGN AND SETTING: A mixed qualitative and quantitative approach data were used to inform this knowledge mobilization project and inform feedback on implementation of the eCOVID-19 RecMap. This approach involved surveying 110 attendees from a public health webinar. Following this webinar, an evidence brief and series of case studies were created and disseminated to 24 Canadian public health practitioners who attended a virtual workshop. This workshop identified barriers and opportunities to improve RecMap use. RESULTS: This study helped to shed light on the needs that public health practitioners have when finding, using, and disseminating public health guidelines. Through the workshop that was conducted, opportunities for public health guidelines can be categorized into 4 categories: 1) information access, 2) awareness, 3) public health development, and 4) usability. Barriers that were identified can also be categorized into 4 categories: 1) usability, 2) information maintenance, 3) public health guidance, 4) awareness. CONCLUSION: This work will help to inform the development and organization of future public health guidelines, and the needs that public health practitioners have when engaging with them.
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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 | 0.132 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".