Development of a toolkit for infection surveillance in long-term care
Bibliographic record
Abstract
Background: Establishing a robust, standardized and validated surveillance system in long-term care (LTC) homes is a necessary strategy to assess and analyze infection trends over time, inform infection prevention and control (IPAC) practices in order to reduce healthcare-associated infections and be compliant with legislative requirements. Methods: To support strong surveillance programs in LTC, a surveillance toolkit was developed and trialed in a LTC corporation consisting of eighteen LTC homes across Southern Ontario. The tool was developed, piloted and trialed using available best practices and revised based on feedback from the LTC IPAC Leads. An evaluation was conducted using formal telephone and in-person interviews, online surveys and informal discussions through regular webinars. Results: Suggestions for improvements to the toolkit included a preference for forms that automated case counting and rate calculations and the removal of tools or sections of tools deemed unnecessary by the user. Conclusion: Although the IPAC Leads did not use all of the tools consistently, they felt the toolkit improved their surveillance process by increasing the standardization and consistency of the tracking of infections.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".