Evaluation of the KA24 (Knowledge Access 24) service for health- and social-care staff in London and the south-east of England. Part 2: qualitative

AIMS AND OBJECTIVES: The aim of this two-part paper is to identify the main transferable lessons learned from both the quantitative and qualitative evaluations of the KA24 (Knowledge Access 24) service of online databases and selected full text journals for health and social care staff in London and the South-East of England. The objectives of the qualitative evaluation were to assess the enablers and barriers to usage, and to assess the impact of the service on patient care. METHODS: Telephone interviews (n=65) and a questionnaire survey (n=296) were conducted with various types of user, in various Trust settings. Some non-users were also contacted. Selection of interviewees and questionnaire recipients was not random, and aimed to cover all groups of users representatively. RESULTS: Results show that policy goals were being delivered, with indications of changes to clinical practice, and improved clinical governance. Promotion, training and support needs to be extensive, and tailored to needs, but users are not always aware they need training. The sharing of passwords cast doubts on the reliability of some usage data. CONCLUSIONS: Digital health library services, delivered at the point of care, are changing the way some clinicians practise. A combination of qualitative and quantitative evaluation methods are needed to assess digital library services

Observation research techniques

This paper outlines the aims of the observation techniques workshop delivered on 11 June 2015, and discusses the experience of the group work sessions. Some participants explained their own experience of observation research, and participants discussed sampling, methods used to collect data and the ethics of observation research. The session participants concluded that observation research was a useful complementary technique for library research. More research published by library and information science practitioners and researchers would help to establish best practice in observation research

Modelling the feeding distribution of wintering pink-footed geese (Anser brachyrhynchus) and Greylag geese (Anser anser) in central Scotland.

Pink-footed and Greylag geese winter in Britain and can cause damage to crops, resulting in a conflict with agriculture. An understanding of where geese are likely to feed would help to target suitable areas for goose management plans, aimed at relieving such conflict. The aim of this project was to create models to predict the feeding distribution of both Pink-footed and Greylag geese. Two separate approaches were taken to model goose feeding distribution from landscape characteristics. The first was a standard approach, logistic regression, which predicted the probability of a field being used by geese from the field's landscape characteristics. Models were based on goose distribution data from field surveys. The main factors affecting field choice by both species were distance from the nearest building and distance from the roost. The inclusion of autologistic terms did not improve the fit of the models. A second, more novel approach to predicting goose distribution was taken to see if more accurate predictions could be produced. This modelling technique involved simulating the movements of Greylag geese throughout the day. The rules constraining goose movement in the model were derived from analysis of radiotracked geese. Flight direction was constrained by altitude or distance from the river while the probability of landing was dependent on the distance from buildings. The accuracy of the models in predicting goose distribution was tested both within the study area, Strathearn and Strathallan, and in another area, Loch Leven. Models based on animal movements have the theoretical advantage of incorporating barriers to movement, but the simulation model did not out-perform the logistic regression model. The models can be applied to other goose feeding areas relatively easily and can be used to identify areas where management plans for both Pink-footed and Greylag geese should be targeted