The COVID-19 Resource Centre: A Tool for Primary Care
Bibliographic record
Abstract
Background: In response to the COVID-19 pandemic, the Ontario-based Centre for Effective Practice (CEP) established the COVID-19 Resource Centre (CRC) in March 2020. This platform rapidly became a critical source of clinical and practice guidance for primary care providers, highlighting the importance of effective information synthesis during public health emergencies. Description: The article discusses the development of the CRC, emphasizing the application of librarianship principles in navigating the challenges posed by the pandemic's information overload and the scarcity of evidence. It outlines the strategies for literature searching, appraisal, and evidence synthesis that were employed to ensure the content's accuracy and utility. The CRC's evolution is presented within the context of its goal to efficiently bridge the gap between evidence and clinical practice, underscoring the collaborative efforts and innovative methodologies that contributed to its success. Outcomes: The CRC has served as an invaluable resource, attracting close to 185,000 visitors from Ontario, across Canada, and internationally. According to survey feedback, 89% of users reported enhanced knowledge of COVID-19 evidence and policies, and 87% stated that the vaccine information directly informed their practice. These statistics underscore the CRC's role in supporting informed decision-making among healthcare providers. Discussion: The CRC marked the CEP's first foray into real-time evidence-based tool development. Facing challenges of expanding information volumes, an unpredictable information landscape, and the need for swift adaptation to new developments, the CRC emerged as a critical resource, enhancing credibility for the CEP, and fostering new partnerships. This journey underscores the importance of librarianship skills-critical appraisal, evidence synthesis, and knowledge translation-in enhancing service delivery.
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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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.023 |
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".