Disconnects in design and infection prevention and control – how the design of products and the environment in neonatal intensive care may be undermining infection prevention practice
Bibliographic record
Abstract
This study examined the role design plays in infection prevention and control within an existing neonatal intensive care unit. Methods from human-centred design such as planning, stakeholder meetings and naturalistic observation were used to obtain infection prevention information related to the existing unit design, interactions with products and the environment, and perspectives of front-line staff on design. Thematic analysis was used to categorize and structure the issues that were identified. The analysis revealed that the design of products and the environment may be undermining best practice in infection prevention. Health care workers experience a variety of difficulties in maintaining the recommended barriers to infection transmission, difficulties which stem from deficiencies in products and the environment. Various aspects of the neonatal care design lack the feedback or supports needed to help health care workers differentiate or work between infection transmission zones making the design challenging to use or maintain in a manner that supports best practice in infection prevention. Identifying issues in the design of products and the environment related to infection prevention practice led to the development of a ‘Design Exploration Guide’. The guide outlines issues and strategies for remediation based on feasibility within the project constraints.
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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 | 0.057 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".