Leveraging modified work program for infection prevention program implementation and professional development
Bibliographic record
Abstract
Background: Since the onset of the COVID-19 pandemic, the daily responsibilities of the Infection Prevention Department (IPD) have been exacerbated by heightened regulatory and licensing requirements, increasing demands from medical staff and patients, and an expanded scope of work. Consequently, infection preventionists (IPs) have struggled to find the bandwidth to effectively implement patient quality care improvement projects. The increased demand for infection prevention and control (IPAC) responsibilities has made it challenging to fill open IP positions. To address this need, a collaborative Modified Work Program (MWP) between the IPD and Human Resource Department at a National Cancer Institute-Designated Comprehensive Cancer Center has proven effective. Modified Staff for Infection Prevention (MSFIP) have been utilized to support IPD daily responsibilities with the potential development of future IPs. Methods: Injured staff were placed on modified duty by the MWP, and the IPD was contacted. An IP interviewed the MSFIP to design appropriate and accommodated responsibilities. Several tools were provided, including helpful guides, daily task lists, links, forms, agency contact information, and references. The MSFIP was granted temporary data security access to electronic medical records used by IPs. Initially, MSFIPs required orientation and shadowing by IPs. Later, MSFIP with longer recovery periods trained new MSFIP. Results: Trained MSFIPs independently managed simple IP daily tasks, allowing IPs to continue and initiate quality improvement projects. Catheter-associated urinary tract infections remained low. All MSFIP expressed a better appreciation of IP work, and several expressed interest in becoming IPs, potentially addressing the replacement of retiring IPs. Conclusion: A well-developed program for MSFIP offers several benefits. IPs should consider using MSFIPs if an MWP exists in their facility, or work on developing one in collaboration with their human resources department.
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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 | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".