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Patient questionnaires are popular tools for assessing and improving service quality, especially as administrators are increasingly expected to consider the patient's voice in their decision making. Despite web‐based questionnaire advantages, they have not been previously compared to telephone questionnaires for assessing quality. The purpose of this paper is to compare telephone questionnaire administration with a web‐based version.

Day surgery patients from a tertiary pediatric hospital completed a telephone interview and a web‐based questionnaire with identical questions. The appropriateness of the web version as a telephone version substitute was ascertained by comparing the number of changes in responses, non‐responses, differences in means, the number of non‐substantive responses and reliability.

The web‐based questionnaire tended towards more negative responses. The mean number of missing responses did not differ between versions, although the web‐questionnaire had more “not sure” responses. Inter‐rater reliability was acceptable.

Parents without internet access were unable to participate.

The web‐based questionnaire is a good substitute for telephone‐administered questionnaires.

The paper shows that parents were able to rate items more candidly owing to the increase in privacy and lack of interviewer bias, which is crucial for improving health service quality.

The purpose of this paper is to propose a noise identification method for data without temporal structure, in which application of typical mathematical white or colored noise models is very limited due to observation order requirements. The method is used to identify the destructive elements and to eliminate them what finally brings prediction improvement.

The paper concerns noise detection problem presented in the framework of ensemble methods via blind signals separation. The authors utilize the Extended Generalized Lambda Distribution (EGLD) model to compare the signals with the target.

The authors proposed novel signals similarity measure which is based on the EGLD system. The authors showed that it can be applied for data with or without time structure, as well as for data which are mutually uncorrelated. It turned out that method is effective for noise identification and can be an alternative, in many cases, to correlation approach, particularly for noise identification problems.

In this method the improvement of prediction results is associated with elimination of the real physical factors rather than mathematical averaging in terms of arbitrary assumed distributions. In this approach, it does not matter what is the structure of aggregated models, what significantly distinct this approach from such techniques as boosting or bagging, in which the aggregation process applies to the models of similar structure. For this reason the methodology is focussed on physical noises elimination from predictions and it is complementary to the other ensemble approaches.