Human factors considerations in designing for infection prevention and control in neonatal care – findings from a pre-design inquiry
Bibliographic record
Abstract
Qualitative data collection methods drawn from the early stages of human-centred design frameworks combined with thematic analysis were used to develop an understanding of infection prevention practice within an existing neonatal intensive care unit. Findings were used to generate a framework of understanding which in turn helped inform a baseline approach for future research and design development. The study revealed that a lack of clarity between infection transmission zones and a lack of design attributes needed to uphold infection prevention measures may be undermining healthcare workers' understanding and application of good practice. The issue may be further complicated by well-intentioned behavioural attitudes to meeting work objectives; undue influences from spatial constraints; the influence of inadvertent and excessive touch-based interactions; physical and/or cognitive exertion to maintain transmission barriers; and the impact of expanding job design and increased workload to supplement for lack of effective barriers. Practitioner Summary: Despite high hand hygiene compliance within a neonatal intensive care unit, healthcare workers expressed concerns about the unit design and infection prevention practice. Early inquiry methods from human-centred design and thematic analysis helped develop a framework to understand how design can be used to aid infection prevention.
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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 | 0.125 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".