Barriers and bridges to infection prevention and control: results of a qualitative case study of a Netherlands' surgical unit
Bibliographic record
Abstract
OBJECTIVES: The objectives of the study are to observe the overall work environment including infection prevention and control (IP&C) practices on the target surgical unit; to analyse the policies and procedures in the hospital and unit environments; to analyse the barriers and bridges to IP&C that practitioners identify in visual narratives of their unit environment and to collect monthly specific IP&C-related anonymised data. DESIGN: In this qualitative case study analysis, a socio-ecological approach on health systems informed the research design and provided a framework to better understand the complexity of implementing effective IP&C. SETTING: The study was conducted on a surgical unit at a Netherlands' hospital that reported successful reductions in the prevalence of targeted multidrug-resistant organisms. METHODS: Research methods included unit observations (n=3), review of relevant policies and procedures, five practitioner-led photo walkabouts of the unit (n=7), three photo elicitation focus groups with practitioners (n=13) and the review of related IP&C data. RESULTS: The findings indicate some conditions and processes present that may influence the low prevalence of multidrug-resistant organisms, including the 'search and destroy' active surveillance strategy, low occupancy rates, a centralised bed cleaning system and the presence of an active grass roots Hygiene in Practice group, which engages practitioners in several ongoing activities to promote IP&C on the units. CONCLUSIONS: Further research on the benefits of practitioner-led community of practices on IP&C practices such as the Hygiene in Practice group is also recommended. Additional case studies to compare theses practices with other acute care hospital around the world would be a valuable way to better understand what IP&C programmes are most effective in which contexts and for what reasons. Further data are available by contacting the primary author directly.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".