Economic Analysis of the Prevention and Control of Nosocomial Infections: Research Protocol
Bibliographic record
Abstract
Background:Nosocomial infections (NIs) are among the main preventable healthcare adverse events. Like all countries, Canada and its provinces are affected by NIs. In 2004, Ministry of Health and Social Services (MSSS) of Quebec instituted a mandatory surveillance NI program for the prevention and control (NIPC) in the hospitals of the province. One target of the MSSS 2015–2020 action plan is to assess the implementation, costs, effects, and return on investment of NIPC measures. This project goes in the same way and is one of the first major studies in Canada to evaluate the efficiency of the NIPC measures. Three objectives will be pursued: evaluate the cost of implementing clinical best practices (CBPs) for infection control; evaluate the economic burden attributable to NIs; and examine the cost-effectiveness of the NIPC by comparing the costs of CBPs against those of NIs. Methods:This project is based on an infection control intervention framework that includes four CBPs: hand hygiene; hygiene and sanitation; screening; and additional precautions. Four medical and surgical units in two hospitals (nonUniversity, University) in the province of Quebec will be studied. The project has four components. Component 1 will construct and content validate an observation grid for measuring the costs of CBPs. Component 2 will estimate CBP costsvia2-week prospective observations of health workers, conducted every 2 months over a 1-year period. Component 3 will evaluate, through a matched case-control study, the economic burden of the four most monitored NIs in Quebec (C-difficile, MRSA, VRE, and CPGNB). Archival patient data will be collected retrospectively. Component 4 will determine the optimal breakeven point for CBPs associated with NIPC. Discussion:This project will produce evidence of the economic analysis of NIPC and give health stakeholders an overview of NIPC cost-effectiveness. It will meet the objectives of the Canadian Patient Safety Institute and the MSSS action plan to analyze the efficiency of NIPC preventive measures. To our knowledge, this is the first such exercise in Quebec and Canada. It will provide governments with a decision support tool through a major empirical study that could be replicated nationally to capture the financial benefits of NIPC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 | 0.064 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.074 | 0.009 |
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".