Barriers and enablers to the implementation of Safewards and the alignment to the i-PARIHS framework - A qualitative systematic review.
Anna BjörkdahlUlf JohanssonLars KjellinVeikko Pelto-PiriPublished in: International journal of mental health nursing (2023)
Inpatient mental healthcare settings should offer safe environments for patients to heal and recover and for staff to provide high-quality treatment and care. However, aggressive patient behaviour, unengaged staff approaches, and the use of restrictive practices are frequently reported. The Safewards model includes ten interventions that aim to prevent conflict and containment. The model has shown promising results but at the same time often presents challenges to successful implementation strategies. The aim of this study was to review qualitative knowledge on staff experiences of barriers and enablers to the implementation of Safewards, from the perspective of implementation science and the i-PARIHS framework. A search of the Web of Science, ASSIA, Cochrane Library, SCOPUS, Medline, Embase, PsycINFO, and CINAHL databases resulted in 10 articles. A deductive framework analysis approach was used to identify barriers and enablers and the alignment to the i-PARIHS. Data most represented by the i-PARIHS were related to the following: local-level formal and informal leadership support, innovation degree of fit with existing practice and values, and recipients' values and beliefs. This indicates that if a ward or organization wants to implement Safewards and direct limited resources to only a few implementation determinants, these three may be worth considering. Data representing levels of external health system and organizational contexts were rare. In contrast, data relating to local (ward)-level contexts was highly represented which may reflect Safewards's focus on quality improvement strategies on a local rather than organizational level.
Keyphrases
- healthcare
- quality improvement
- primary care
- systematic review
- patient safety
- big data
- electronic health record
- mental health
- end stage renal disease
- randomized controlled trial
- magnetic resonance imaging
- chronic kidney disease
- newly diagnosed
- magnetic resonance
- pain management
- machine learning
- meta analyses
- artificial intelligence
- long term care
- case report
- chronic pain
- peritoneal dialysis
- affordable care act