The present results supported the use of visual analogue scale rather than Likert scaling in patient satisfaction surveys and stressed the need to account for as many potential confounding factors as possible.

The study aimed to identify methodological confounding factors affecting patient satisfaction survey results. The data gathered from CINAHL and PubMed databases consisted of 355 surveys published from 2006 to 2012. Linear regression and Bayesian models, with seven potential survey-related confounders together with patient age and gender as explanatory variables, were constructed. According to the linear model, up to 12% of the original variation in patient satisfaction was explained by confounding variables, not by the actual variation in satisfaction. The presence of an interviewer resulted in lower satisfaction levels, and the satisfaction results correlated negatively with the number of items in the questionnaire. According to the Bayesian model, if patients were over 60 years old and the questionnaire consisted mainly of positively phrased items, the probability of rating their experiences as very satisfied was 75%. The Bayesian and linear models endorsed each other and revealed specifically that the surveys reporting high patient satisfaction could be predicted on the basis of confounding variables. The following recommendations are given for constructing a patient satisfaction survey: use neutral rather than negatively or positively phrased items, and use enough items to increase the likelihood that the least satisfactory care components are also included in order to better enable comparisons across sporadic surveys.

A rational response to the variations in patient care needs and intensity in the complex care environment is flexible nurse staffing. Increasing nursing hours per patient day to achieve shorter length of stays is not the only solution, well-functioning care processes are also essential.

Flexible nurse staffing is preferable to fixed staffing to provide patients with shorter length of stay in acute care units. In the present research, the Bayesian method revealed non-linear relationships between nurse staffing and patient and care outcomes.

Survey-based information on nurse job satisfaction can be modelled with data-based nurse staffing indicators. Nurse researchers could use the Bayesian approach to obtain information about the effects of nurse staffing on nursing outcomes.