Using the number of licensed beds as the sole criterion for infection prevention and control lead staffing in long-term care settings; the case of Ontario, Canada
Bibliographic record
Abstract
In April 2022, the Ministry of Long-Term Care (LTC) in Ontario, Canada released the infection prevention and control (IPAC) standard as part of the requirements under a new legislation referred to as the Fixing Long-Term Care Act, 2021. This was enacted to address gaps in the sector that were exposed during the COVID-19 pandemic. The main aspect of the infection prevention and control (IPAC) standard within the Act, requires each long-term care home (LTCH) to have an IPAC lead whose primary responsibility is the home’s IPAC program. As part of the regulation, every licensee shall ensure that the IPAC Lead works regularly in that position on-site at the home for at least the following minimum hours based on the licensed bed capacity: for homes with a licensed bed capacity of 69 beds or fewer at least 17.5 hours per week; for homes with a licensed bed capacity of more than 69 beds but fewer than 200 beds, at least 26.25 hours per week; for homes with a licensed bed capacity of 200 beds or more, at least 35 hours per week (FLTCA 2021). Section 102 (7) of the regulation further elaborates on the broad responsibilities of the IPAC lead including: working with the interdisciplinary IPAC team to implement the IPAC program; managing and overseeing the program; overseeing the delivery of IPAC education to all staff, caregivers, volunteers, visitors, and residents; and auditing of IPAC practices in the home. In meeting regulatory compliance, most homes in the province of Ontario are now basing their staffing of the IPAC lead position solely on the minimum hours required under the regulation. There is no doubt that, historically, the ratio of infection control professionals (ICPs) in acute care settings has been tied to inpatient beds with no regard on the variability in facility complexity. For example, the Study of the Efficacy of Nosocomial Infection Control (SENIC) in the United States that evaluated the nosocomial IPAC programs from 1970 to 1976 recommended 1 infection control nurse per 250 beds (Haley et al, 1985). Another study suggested a ratio of 0.8 – 1.0 ICP per 100 occupied acute care beds (O’Boyle et., 2002) and a more recent study that aggregated data across organizations recommended a new benchmark of 1.0 ICP full-time equivalent per 69 beds if ambulatory, LTC, or home care are included (Bartles et al, 2018).
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".