Development and validation of a time and motion guide to assess the costs of prevention and control interventions for nosocomial infections: A Delphi method among experts
Bibliographic record
Abstract
BACKGROUND: Nosocomial infections place a heavy burden on patients and healthcare providers and impact health care institutions financially. Reducing nosocomial infections requires an integrated program of prevention and control using key clinical best care practices. No instrument currently exists that measures these practices in terms of personnel time and material costs. OBJECTIVE: To develop and validate an instrument that would measure nosocomial infection control and prevention best care practice costs, including estimates of human and material resources. METHODS: An evaluation of the literature identified four practices essential for the control of pathogens: hand hygiene, hygiene and sanitation, screening and additional precaution. To reflect time, materials and products used in these practices, our team developed a time and motion guide. Iterations of the guide were assessed in a Delphi technique; content validity was established using the content validity index and reliability was assessed using Kruskall Wallis one-way ANOVA of rank test. RESULTS: Two rounds of Delphi review were required; 88% of invited experts completed the assessment. The final version of the guide contains eight dimensions: Identification [83 items]; Personnel [5 items]; Additional Precautions [1 item]; Hand Hygiene [2 items]; Personal Protective Equipment [14 items]; Screening [4 items]; Cleaning and Disinfection of Patient Care Equipment [33 items]; and Hygiene and Sanitation [24 items]. The content validity index obtained for all dimensions was acceptable (> 80%). Experts statistically agreed on six of the eight dimensions. DISCUSSION/CONCLUSION: This study developed and validated a new instrument based on expert opinion, the time and motion guide, for the systematic assessment of costs relating to the human and material resources used in nosocomial infection prevention and control. This guide will prove useful to measure the intensity of the application of prevention and control measures taken before, during and after outbreak periods or during pandemics such as COVID-19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".