Implementation of a nursing oral health care protocol in a university teaching hospital:A cluster-randomized stepped-wedge design

Introduction: Providing oral care is an essential part of basic nursing care but receives little priority in daily practice, with a risk of adverse events. Also, nurses report many barriers to adequate provision of oral care, such as time restraints, insufficient materials, fear of causing pain, lack of knowledge and a negative attitude towards providing oral care. Methods: We performed a cluster-randomized, stepped-wedge study to explore the effect of the the implementation of a new nursing evidence-based oral care protocol on nurses' knowledge, attitude and protocol adherence. The study population included both nursing students, graduated nurses and patients in selected wards. The implementation strategy included oral and written information, instruction videos and reminders. Nurses' knowledge and attitude towards oral care were assessed at baseline and after the implementation of the protocol with a validated 47-item questionnaire with a score range of 0–100. Secondarily, nurses' protocol adherence to teeth brushing, measured in Activities of Daily Living (ADL) dependent patients, was evaluated. The Standards for Reporting Implementation Studies (StaRI) Statement was used. Results: At baseline, the questionnaire was completed by 226 nurses; after implementation by 283. Knowledge had significantly improved from 68.8 to 72.3. Nurses' attitude improved not significantly. Protocol adherence was assessed in 73 ADL-dependent patients at baseline, in 51 after implementation. Adherence to teeth brushing significantly decreased in patients with permanent teeth. Also, adherence to both teeth brushing and usage of soap decreased in patients with (partial) dentures. Conclusion: Nurses' knowledge and attitude of oral care increased somewhat after the implementation of a new nursing evidence-based protocol. After implementation, there was an unexplained decreased adherence to oral care in ADL-dependent patients.</p

Systematic RADaR analysis of responses to the open-ended question in the Culture of Care Barometer survey of a Dutch hospital

OBJECTIVES: Systematically measuring the work environment of healthcare employees is key to continuously improving the quality of care and addressing staff shortages. In this study, we systematically analyse the responses to the one open-ended question posed in the Dutch version of the Culture of Care Barometer (CoCB-NL) to examine (1) if the responses offered new insights into healthcare employees' perceptions of their work environment and (2) if the original CoCB had any themes missing. DESIGN: Retrospective text analysis using Rigorous and Accelerated Data Reduction technique. SETTING: University hospital in the Netherlands using the CoCB-NL as part of the annual employee survey. PARTICIPANTS: All hospital employees were invited to participate in the study (N=14 671). In total, 2287 employees responded to the open-ended question. RESULTS: 2287 comments were analysed. Comments that contained more than one topic were split according to topic, adding to the total (n=2915). Of this total, 372 comments were excluded because they lacked content or respondents indicated they had nothing to add. Subsequently, 2543 comments were allocated to 33 themes. Most comments (n=2113) addressed the 24 themes related to the close-ended questions in the CoCB-NL. The themes most commented on concerned questions on 'organisational support'. The remaining 430 comments covered nine additional themes that addressed concerns about work environment factors (team connectedness, team effectiveness, corporate vision, administrative burden and performance pressure) and themes (diversity and inclusion, legal frameworks and collective bargaining, resilience and work-life balance, and personal matters). CONCLUSIONS: Analysing responses to the open-ended question in the CoCB-NL led to new insights into relevant elements of the work environment and missing themes in the COCB-NL. Moreover, the analysis revealed important themes that not only require attention from healthcare organisations to ensure adequate improvements in their employees' work environment but should also be considered to further develop the CoCB-NL.</p

Patient and Disease Characteristics Associated with Activation for Self-Management in Patients with Diabetes, Chronic Obstructive Pulmonary Disease, Chronic Heart Failure and Chronic Renal Disease : A Cross-Sectional Survey Study

A substantial proportion of chronic disease patients do not respond to self-management interventions, which suggests that one size interventions do not fit all, demanding more tailored interventions. To compose more individualized strategies, we aim to increase our understanding of characteristics associated with patient activation for self-management and to evaluate whether these are disease-transcending. A cross-sectional survey study was conducted in primary and secondary care in patients with type-2 Diabetes Mellitus (DM-II), Chronic Obstructive Pulmonary Disease (COPD), Chronic Heart Failure (CHF) and Chronic Renal Disease (CRD). Using multiple linear regression analysis, we analyzed associations between self-management activation (13-item Patient Activation Measure; PAM13) and a wide range of socio-demographic, clinical, and psychosocial determinants. Furthermore, we assessed whether the associations between the determinants and the PAM were disease-transcending by testing whether disease was an effect modifier. In addition, we identified determinants associated with low activation for self-management using logistic regression analysis. We included 1154 patients (53% response rate); 422 DM-II patients, 290 COPD patients, 223 HF patients and 219 CRD patients. Mean age was 69.6 +/- 10.9. Multiple linear regression analysis revealed 9 explanatory determinants of activation for self-management: age, BMI, educational level, financial distress, physical health status, depression, illness perception, social support and underlying disease, explaining a variance of 16.3%. All associations, except for social support, were disease transcending. This study explored factors associated with varying levels of activation for self-management. These results are a first step in supporting clinicians and researchers to identify subpopulations of chronic disease patients less likely to be engaged in self-management. Increased scientific efforts are needed to explain the greater part of the factors that contribute to the complex nature of patient activation for self-management

Datafile Patient activation for self-management

This data set belongs to the manuscript “Patient and disease characteristics associated with activation for self-management in patients with diabetes, chronic obstructive pulmonary disease, chronic heart failure and chronic renal disease: a cross-sectional survey study”. The dataset is based on a survey consisting of patient characteristics and different health related questionnaires. For explanations of the variables see the Readme file

Linear regression analyses—univariable associations of the determinants with activation for self-management.

<p>CI = Confidence interval, BMI = Body Mass Index, DM-II = Diabetes Mellitus type II, COPD = Chronic Obstructive Pulmonary Disease, CHF = Chronic Heart Failure, CRD = Chronic Renal Failure.</p><p>Linear regression analyses—univariable associations of the determinants with activation for self-management.</p

Flowchart of recruitment.

<p>Flowchart of recruitment.</p

Multivariable logistic regression analyses—determinants associated with poor activation for self-management.

<p>CI = Confidence interval, OR = Odds ratio, CI = confidence interval, BMI = Body mass index, SF-12 = short form-12, HADS = Hospital anxiety and depression scale, DM-II = Diabetes Mellitus type II, COPD = Chronic Obstructive Pulmonary Disease, CHF = Chronic Heart Failure, CRD = Chronic Renal Failure.</p><p>Multivariable logistic regression analyses—determinants associated with poor activation for self-management.</p

Distribution of PAM (patient activation measure) scores stratified by underlying chronic condition.

<p>DM-II = diabetes mellitus type 2, COPD = Chronic obstructive pulmonary disease, CHF = Chronic heart failure, CRD = Chronic renal disease.</p