Children's unscheduled primary and emergency care in Ireland: a multimethod approach to understanding decision making, trends, outcomes and parental perspectives (CUPID): project protocol.

Introduction: The aim of this project is to determine the patterns, decision-making processes and parental preferences associated with unscheduled paediatric healthcare utilisation in Ireland. Unscheduled paediatric healthcare is outpatient care provided within primary care settings by general practitioners (GPs), emergency departments (EDs) located in paediatric and general hospitals, and out-of-hours services provided by cooperatives of GPs operating on a regional basis. This project will take a multimethod approach to analysing the utilisation of unscheduled paediatric healthcare nationally within the context of a significant change to the provision of healthcare for young children in Ireland-the introduction of free at the point of delivery GP care for all children aged under 6.

Factors that influence family and parental preferences and decision making for unscheduled paediatric healthcare: a systematic review protocol.

There is a plethora of factors that dictate where parents and families choose to seek unscheduled healthcare for their child; and the complexity of these decisions can present a challenge for policy makers and healthcare planners as these behaviours can have a significant impact on resources in the health system. The systematic review will seek to identify the factors that influence parents' and families' preferences and decision making when seeking unscheduled paediatric healthcare.  Five databases will be searched for published studies (CINAHL, PubMed, SCOPUS, PsycInfo, EconLit) and grey literature will also be searched.

The importance of regional models in assessing canine cancer incidences in Switzerland.

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally.