Respiratory healthcare professionals’ views on long-term recommendations of interventions to prevent acute respiratory illnesses after the COVID-19 pandemic
Bibliographic record
Abstract
Physicians work in complex adaptive systems. For complex medical problems, physicians are often quick to seek solutions before understanding the problems before them. This is due to engrained mental models of hierarchies of evidence that often fail to recognize the practical difficulties in translating evidence into changes in medical practice. There is a need to understand complexity and implications for implementation, spread and scale, evaluation, and knowledge translation. This workshop is designed to give participants a more nuanced understanding of solving complex problems in medicine and a heuristic framework to advance medical practice. The workshop will be interactive as participants learn and create their own hypothetical strategy to identify a problem, conduct research, and translate evidence into practice. This interactive workshop will give participants the opportunity to: 1) learn the Edmonton Physician Learning Program approach to mobilize the power of a Wicked Team to understand complex medical problems and create elegant solutions; 2) understand how to use the theory and frameworks to make sense of problems, support behaviour change, and optimize implementation; and 3) begin to harness the power of adaptive skill building to support communication, team effectiveness, and getting the job done. Participants will be presented with a Quality Improvement research problem that we at the Edmonton Physician Learning Program have recently completed as a project. The problem, identified in collaboration with antimicrobial stewardship leaders in Edmonton, was that cefazolin--the antibiotic of choice for all patients undergoing surgery, including those with a beta-lactam allergy--is under-administered within surgical procedures. Cefazolin is structurally different from all other beta-lactam antibiotics and therefore does not cross-react with any other beta-lactam. This was identified as ideal for Quality Improvement research because cefazolin is highly effective, safe, and can reduce the likelihood of surgical site infections by upwards of 50%. Participants will be guided through the workshop to design their own research and team approach to address this problem, beginning with the question: why does this problem matter? Participants will proceed to organize a hypothetical research team, research design, and implementation strategies to advance physician practice. At the end, participants will learn how the Edmonton Physician Learning Program solved this problem.
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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 | 0.054 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".