Evaluation of the Performance and Resource Needs of a Construction Infection Prevention and Control Program
Bibliographic record
Abstract
Background: The University Health Network (UHN) is a multisite, academic health sciences center in Toronto, Canada, with 1,300 inpatient beds and ∼126,000 emergency department visits annually. Clinical services include a transplant program, cancer center, dialysis units, and rehabilitation sites. Currently, ∼0.83 km2 (>9 million ft2) of UHN real estate, ∼200 construction, renovation and maintenance projects are underway. The UHN Construction Infection Control Program (CICP) was created in 2012 and has expanded to include 3.5 FTEs to meet the needs of infection prevention oversight during these activities. We describe the performance indicators for the UHN CICP between May 2016 and December 2018 that have informed productivity and resource needs. Methods: Since 2016, construction infection preventionists (CIPs) have prospectively collected data on the frequency of activities reflecting CIP productivity and core job functions: number of meetings (attended and missed), site inspections, responses to breaches in control measures, education hours delivered, urgent requests, and after-hours work. Annual activity rates (frequency of activity divided by CIP months) were analyzed for trends, accounting for additions in CICP personnel over time. Results: Human resources and activities performed in the CICP from 2016 to 2018 are outlined in Table 1. As CICP human resources increased, the number of initiatives supported by the CICP team rose. Activity rates for attended meetings, inspections and hours of education provided increased with higher CIP resources, suggesting an improvement in individual productivity of each CIP (Fig. 1). Concurrently, the rate of missed meetings declined and after-hour requests and breach responses remained stable. Conclusions: An appropriately staffed CICP for the volume and risk level of organization-wide construction, renovation, and maintenance activities is crucial to infection prevention. We developed performance indicators based upon key functions of CIPs to evaluate the productivity of our team and ensure we had adequate human resources to maintain patient safety through our evolving needs. Funding: None Disclosures: Susy Hota reports contract research for Finch Therapeutics.
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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 | 0.032 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".