An ethical framework adapted for infection prevention and control
Bibliographic record
Abstract
OBJECTIVE: The ethical implications of infection prevention and control (IPAC) are recognized, yet a framework to guide the application of ethical principles is lacking. We adapted an ethical framework to provide a systematic approach for fair and transparent IPAC decision making. METHODS: We conducted a literature search for existing ethical frameworks in IPAC. Working with practicing healthcare ethicists, an existing ethical framework was adapted for use in IPAC. Indications were developed for application to practice, with integration of ethical principles and process conditions specifically relevant to IPAC. Practical refinements were made to the framework based on end-user feedback and application to 2 real-world situations. RESULTS: In total, 7 articles were identified that discussed ethical principles within IPAC, but none proposed a systematic framework to guide ethical decision making. The adapted framework, named the Ethical Infection Prevention and Control (EIPAC) framework, takes the user through 4 intuitive and actionable steps, centering key ethical principles that facilitate reasoned and just decision making. In applying the EIPAC framework to practice, weighing the predefined ethical principles in different scenarios was a challenge. Although no hierarchy of principles can apply to all contexts in IPAC, our experience highlighted that the equitable distribution of benefits and burdens, and the proportional impacts of options under review, are particularly important considerations for IPAC. CONCLUSIONS: The EIPAC framework can serve as an actionable ethical principles-based decision-making tool for use by IPAC professionals encountering complex situations in any healthcare context.
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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 | 0.158 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".