Bibliographic record
Abstract
Abstract: The World Health Organization (WHO) defines Health Care-Associated Infection (HCAI) as an infection a patient acquired in health care settings. In Canada, more than 220,000 patients are infected by HCAIs annually, with 8,500 to 12,000 of these patients resulting in death, thus becoming the fourth leading cause of death for Canadians. Hand hygiene practice is the most critical measure to prevent HCAIs, however, research indicates that in hospitals worldwide, just 40% of health care workers abide by the advised hand hygiene guidelines. A new effective HCAI control and prevention program is needed to sustain benefits, building on prior interventions such as including hand hygiene education that stresses the necessity of this practice in the protocol, providing factual proof of the effectiveness of hand hygiene, the acknowledgment by senior staff of their responsibility as role models for all staff, innovative technological methods, and regular auditing/feedback. With the current outbreak of coronavirus disease (COVID-19) that has infected millions around the world, a new HCAI control and prevention program can increase the compliance rate of handwashing with alcohol-based hand rub/sanitizer amongst healthcare professionals thus aiding in prevention and control of spread within the community.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.260 | 0.116 |
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".