Measuring and Monitoring Healthcare-Associated Infections: A Canadian Collaboration to Better Understand the Magnitude of the Problem
Bibliographic record
Abstract
Patients should never have to worry about getting an infection while in hospital. Yet every year, many hospitalized Canadians continue to acquire an infection during their hospital stay and experience increased morbidity and mortality as a result of these healthcare-associated infections (HAIs) (PHAC 2019b).Measuring and monitoring HAIs provide key data to better understand the magnitude of the problem.In Canada, there are inconsistencies in the use of standardized HAI case definitions and surveillance practices.These inconsistencies make it difficult to provide benchmarks and set targets to help reduce the rate of HAIs in Canadian hospitals. Challenges with Measurement and Surveillance in CanadaDetermining the scope of the problem is the necessary first step to formulating an effective infection prevention and control response to HAIs.Surveillance is "the ongoing, systematic collection, analysis, and interpretation of health data … integrated with the timely dissemination of these data to those who need to know" (Centers for Disease Control 1986).Strengthening surveillance is critical as it is the basis
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.
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 | 0.032 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".