Surveillance for health care–associated infections, antimicrobial resistant organisms and antimicrobial use in Canadian long-term care homes: A cross-sectional survey
Bibliographic record
Abstract
Background: Our understanding of health care–associated infection (HAI), antimicrobial resistant organism (ARO), and antimicrobial use (AMU) surveillance activities across Canadian long-term care homes (LTCHs) is limited, in part because nationwide surveillance in this setting has yet to be established. Methods: To address this knowledge gap, the Canadian Nosocomial Infection Surveillance Program administered a 12-item cross-sectional survey to LTCHs across all provinces and territories in English and French. LTCHs were defined as government-licensed homes for individuals with medical needs who require 24-hour onsite access to registered nurse care and/or treatment. Results: Between June 1 and November 28, 2023, 770 of an estimated 2,076 LTCHs responded to the survey (37%). Of the respondents, 41% (318/770) were publicly funded, 67% (504/758) had between 51 and 200 long-term care beds, and 92% (694/758) reported having a designated person who leads infection prevention and control. The majority of LTCHs reported conducting outbreak surveillance (680/713, 95%) and surveillance for at least one type of HAI (672/740, 91%). The most common HAIs under surveillance were urinary tract infection (576/725, 79%), Clostridioides difficile infection (546/725, 75%), and gastroenteritis (545/725, 75%). Half of the LTCHs reported testing new residents for AROs via pre-admission or admission cultures (368/713, 52%). Almost two-thirds (441/703, 63%) reported monitoring systemic antibiotic use. Conclusions: Despite differences in the scope of surveillance activities, mechanisms to measure the burden of HAIs and AROs in this setting exist and may provide the foundation for future national surveillance activities. Generalizability to all Canadian LTCHs is uncertain due to possible sampling, non-response, and social desirability biases.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".