ArticleFrontiers in health services2022
Tailoring implementation interventions of different order in infection prevention and control: A cascadic logic model (IPC-CASCADE).
Article in Frontiers in health services, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- A pragmatic randomized trial to compare strategies for implementing primary HPV testing for routine cervical cancer screening in a large healthcare system.Implementation science : IS · 2025Trial
- Evaluation of awareness, perceptions, and compliance with infection prevention and control protocols among healthcare professionals at a tertiary hospital in Nasarawa State, Nigeria.Antimicrobial resistance and infection control · 2026Article
- Preferences for Infection Prevention in Hospital-Acquired Infections.JAMA network open · 2026Article
- Learning motivation and self-assessment in health economics: a survey on overconfidence in healthcare providers.BMJ open · 2024Article
- Article
- Precision prevention in occupational health: a conceptual analysis and development of a unified understanding and an integrative framework.Frontiers in public health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Implementation interventions in infection prevention and control (IPC) differ by recipients. The two target groups are healthcare workers directly involved in patient care ("frontline") and IPC professionals as proxy agents, that is, implementation support practitioners. While both types of implementation interventions aim to promote compliance with clinical interventions to prevent healthcare-associated infections (HAI), their tailoring may be vastly different, for example, due to different behavioural outcomes. Additionally, IPC teams, as recipients of empowering tailored interventions, are under-researched. To overcome this gap and improve conceptual clarity, we proposed a cascadic logic model for tailored IPC interventions (IPC-CASCADE). In the model, we distinguished between interventions by IPC professionals targeting clinicians and those targeting IPC professionals (first- and second-order implementation interventions, respectively). Tailoring implies selecting behaviour change techniques matched to prospectively-assessed determinants of either clinician compliance (in first-order interventions) or interventions by IPC professionals for frontline workers (in second-order interventions). This interventional cascade is embedded in the prevailing healthcare system. IPC-CASCADE is horizontally structured over time and vertically structured by hierarchy or leadership roles. IPC-CASCADE aims to highlight the potential of increasing the impact of tailored interventions
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.