Research priorities in infection prevention and control in Alberta, Canada: a modified Delphi process
Bibliographic record
Abstract
BACKGROUND: Infection prevention and control (IPC) measures are critical to reduce the risk of healthcare-associated infections. AIM: To identify, by consensus, specific IPC research questions where IPC evidence gaps exist, in the setting of Alberta Health Services/Covenant Health, Canada. METHODS: A multidisciplinary committee informed the consensus process. Individuals with expertise in IPC (Delphi panel) identified an initial set of questions (Delphi Round 1). Questions were grouped into common themes and categories. The committee conducted an interim prioritization to short-list the research questions. Using snowball sampling, local and national IPC partners ranked each research question by the level of importance and added questions missed in previous rounds (Delphi Round 2). The consensus meeting included elements from the James Lind Alliance and the Nominal Group Technique to prioritize the research questions. Participants included: infection control professionals; IPC leadership; physicians in IPC, infectious diseases, and microbiology; epidemiologists; analysts; government officials; quality and safety, and antimicrobial stewardship representatives; researchers; patients/family advisors. FINDINGS: There were 159 initial questions, with 63 categorized as research questions. Following interim prioritization and the second Delphi round, 21 questions were presented at the consensus meeting. The top ten research questions fell into five themes: behavioural science strategies with healthcare workers, impact of the patient environment, IPC guideline evaluation, intervention effectiveness, and surveillance and monitoring. CONCLUSION: This consensus exercise identified IPC research questions that were important to healthcare workers, healthcare leaders, researchers, and patients. This work may generate a pan-Canadian dialogue to develop a national research agenda for IPC.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".