Changing nursing behavior in healthcare

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We will conduct a theoretical analysis of included studies using an amended version of the Theory Coding Scheme [ 25 ]. As Garnett et al.

The amended Theory Coding Scheme has a total of 17 items three of which have sub-items see Additional file 5. We will resolve differences through discussion, and we will involve another review author if a consensus is not reached. Rounds of testing will be performed initially until the inter-rater reliability IRR reaches a substantial level of agreement prevalence-adjusted bias-adjusted kappa PABAK statistic greater or equal to.

A total theory use score will be calculated i. A higher score will be indicative of a highest degree of theory use. We will code the mechanisms of action of behaviour change in clinical practice targeted by implementation interventions using coding guidelines from Michie and colleagues [ 18 , 19 , 20 ]. We will use the labels and definitions of the 26 mechanisms of action listed on the Theory and Technique Tool www. Each mechanism of action will be coded as either present 1 or absent 0 in the experimental and comparator interventions.

Ten review authors in teams of two will code each study for mechanisms of action independently, differences will be resolved through discussion and we will involve another review author if a consensus is not reached. We will use the labels, definitions and examples of the 93 behaviour change techniques included in the Behaviour Change Technique Taxonomy v1 [ 21 ] to code studies for behaviour change techniques. In addition, we will use the coding tool developed by Pearson, Byrne-Davis [ 37 ] illustrating behaviour change techniques applied to health professional training.

A coding manual and instructions will be given to review authors. Review authors involved in the behaviour change technique coding will complete the Behaviour Change Technique Taxonomy Online Training www.

The training, lasting approximately 6 h, is a resource where researchers can familiarize themselves with behaviour change technique labels, definitions and examples, and learn how to accurately, reliably and confidently apply the taxonomy.

Behaviour change technique coding will be conducted using NVivo version 12 [ 34 ]. Ten review authors in teams of two will code each study for behaviour change techniques independently, differences will be resolved through discussion and we will involve another review author if a consensus is not reached. Ten review authors in teams of two will assess risk of bias independently for each study using the criteria outlined in the revised Cochrane Collaboration Risk of Bias Tool RoB 2.

Any disagreement will be resolved by discussion or by involving another review author. For individually randomized trials including crossover trials and non-randomized controlled trials, we will assess the risk of bias according to the following domains: 1 bias arising from the randomization process; 2 bias due to deviations from intended interventions; 3 bias due to missing outcome data; 4 bias in measurement of the outcome; 5 bias in selection of the reported result.

For cluster-randomized trials, we will include an additional domain: 1b bias arising from identification or recruitment of individual participants within clusters. Non-randomized studies will be considered at high risk of bias. We will not exclude studies on the grounds of their risk of bias but we will report them when presenting the results of the studies. We anticipate the inclusion of cluster RCTs. Thus, we will evaluate the analysis methods of these studies by determining the level of analysis and if statistical corrections were used e.

We will conduct analyses adjusting for clustering if we observe unit-of-analysis issues by dividing the original sample size by the design effect, as suggested by the Cochrane Handbook for Systematic Reviews of Interventions [ 27 ]. For studies with multiple intervention groups, we will include each pairwise comparison relevant to this review separately, but with shared intervention groups divided out approximately evenly among the comparisons [ 27 ].

We will contact investigators to obtain missing data when necessary. In the case where investigators do not answer our request, data imputation will be performed using the statistical formulas recommended by the Cochrane Handbook for Systematic Reviews of Intervention [ 27 ] when applicable.

In the case where missing outcome data cannot be obtained and data imputation cannot be performed, we will exclude the study for the outcome in question.

We will assess heterogeneity by examining the characteristics of included studies, the similarities and disparities between the types of participants, the types of interventions and the types of outcomes. We will then use the chi-square statistic and the I 2 to assess statistical heterogeneity for analyses including two studies or more within the Review Manager RevMan software version 5. For the chi-square statistic, we will use a statistical significance level p value of 0.

A statistically significant result will indicate a problem of heterogeneity [ 27 ]. For the I 2 statistic, as suggested by Higgins et al. We will assess reporting biases using funnel plots if more than 10 studies are included in the meta-analysis for a specific outcome.

We will follow the guidelines regarding funnel plot asymmetry as described in the Cochrane Handbook for Systematic Reviews of Interventions [ 27 ]. We will synthesize the characteristics of included studies at four levels—i. We will quantify the types—i.

We will ensure that an increase in scores for continuous outcomes can be interpreted in the same way for each outcome, and report where the directions will be reversed if this is necessary. We will undertake meta-analyses that will compare changes between intervention and control participants in primary and secondary outcomes only if: 1 the implementation interventions, targeted clinical practices and the underlying clinical question are similar enough for pooling to make sense; 2 there is at least two studies available for each outcome of interest.

Meta-analyses will be conducted in RevMan version 5. We will define a statistically significant result by a two-sided alpha of 0. If it is not possible to conduct a meta-analysis, we will present a narrative summary of the results. We will undertake random-effects meta-regression analyses if at least 10 studies report enough data to compute a SMD regarding the primary outcome clinical practice change.

We will conduct meta-regression analyses to: 1 examine the association between the Theory Coding Scheme covariates i. Meta-regression analyses will serve to investigate unexplained heterogeneity in the SMDs between studies.

Each study will be weighted in the regression models using the inverse of its variance; studies with the lowest amount of variance will be given a bigger weight in the regression model than those with the largest amount of variance. Two review authors will assess the quality of the evidence independently for each outcome according to the five domains risk of bias, inconsistency, indirectness, imprecision, publication bias established by the Grading of Recommendations Assessment, Development, and Evaluation GRADE guidelines [ 45 ].

We plan to carry out subgroup analyses to investigate heterogeneity when ten or more studies are available in the underlying outcome. If there are a sufficient number of studies, we will explore the following potential effect modifiers:. Implementation intervention types according to EPOC taxonomy [ 12 ];. We will conduct a sensitivity analysis by excluding studies deemed at high risk of bias.

We will also conduct a sensitivity analysis to exclude studies with imputed data. Results of this systematic review, meta-analysis and meta-regression will inform knowledge users e. In addition, data regarding the theory use, targeted mechanisms of action and included behaviour change techniques in studies will be useful for reporting, replicating and synthesizing evidence.

Results will be disseminated through publications, conference presentations, website postings and interactive knowledge exchange events with key stakeholders. This review has potential limitations. First, this review will build exclusively on published studies, whereas unpublished studies, grey literature and non-peer-reviewed literature will be excluded. Although including unpublished, grey and non-peer-reviewed literature has potential benefits in terms of comprehensiveness, it can introduce bias in the results of the systematic review and meta-analysis.

Unpublished studies are usually of lower methodological quality than published studies [ 47 ]. Thus, we will conduct a meta-analysis using the SMD. This will allow us to standardize the results of studies to a uniform scale before pooling them. However, this method also has downsides since it assumes that the differences in standard deviations among studies reflect differences in measurement scales and not differences in variability among study populations [ 27 ]. Review authors deemed the use of the SMD appropriate for this review since it focuses on nurses, minimizing the risk of bias.

Other types of implementation interventions e. However, we believe these interventions differ in scope and deserve their own review. World Health Organization. The update, Global Health Workforce Statistics.

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Download references. The authors would like to thank all the participating physicians, registered nurses and assistant nurses who participated in the interviews. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. You can also search for this author in PubMed Google Scholar.

All analysed the data. PN drafted the manuscript, but it was reviewed and critically revised for important intellectual content by all authors. All authors read and gave final approval of the version of the manuscript submitted for publication. Correspondence to Per Nilsen. All the participants gave their written and oral consent to participate in the interviews. The study was performed according to World Medical Association Declaration of Helsinki ethical principles for medical research involving human subjects.

To maintain the principle of non-maleficence, the participants were guaranteed confidentiality, which was taken into account when reporting the findings through abstracted findings presented at the group level. In the interviews, the researchers were aware of power issues, in that an interview is not a conversation between two equal individuals.

The interview time was taken into careful consideration. The participants were given opportunity to reflect on what they said in the interviews, and time was also available for the participants to ask questions.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions. Nilsen, P. Characteristics of successful changes in health care organizations: an interview study with physicians, registered nurses and assistant nurses.

Download citation. Received : 19 November Accepted : 14 February Published : 27 February Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Research article Open Access Published: 27 February Characteristics of successful changes in health care organizations: an interview study with physicians, registered nurses and assistant nurses Per Nilsen 1 , Ida Seing 2 , Carin Ericsson 1 , 3 , Sarah A.

Abstract Background Health care organizations are constantly changing as a result of technological advancements, ageing populations, changing disease patterns, new discoveries for the treatment of diseases and political reforms and policy initiatives. Methods The study was based on semi-structured interviews with 30 health care professionals: 11 physicians, 12 registered nurses and seven assistant nurses employed in the Swedish health care system.

Results The analysis yielded three categories concerning characteristics of successful changes: having the opportunity to influence the change; being prepared for the change; valuing the change. Conclusions Organizational changes in health care are more likely to succeed when health care professionals have the opportunity to influence the change, feel prepared for the change and recognize the value of the change, including perceiving the benefit of the change for patients.

Background The only constant in health care organizations, as the saying goes, is change. Methods Study setting, design and participants Study data come from interviews with Swedish health care professionals physicians, registered nurses, assistant nurses. Table 1 Participant characteristics Full size table. Having the opportunity to influence the change The health care professionals emphasized the importance of having the opportunity to influence organizational changes that are implemented.

Discussion Change is pervasive in modern health care. Conclusions In conclusion, organizational changes in health care are more likely to succeed when health care professionals have the opportunity to influence the change, feel prepared for the change and recognize the value of the change, including perceiving the benefit of the change for patients. Availability of data and materials All interview data analysed during the current study are available from the corresponding author on reasonable request.

Birken US: United States. Google Scholar SKL. Google Scholar Gray M. Google Scholar Gadolin C.

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