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Using marketing theory to inform strategies for recruitment: a recruitment optimisation model and the txt2stop experience

Overview of attention for article published in Trials, May 2014
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6 X users

Citations

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82 Mendeley
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Title
Using marketing theory to inform strategies for recruitment: a recruitment optimisation model and the txt2stop experience
Published in
Trials, May 2014
DOI 10.1186/1745-6215-15-182
Pubmed ID
Authors

Leandro Galli, Rosemary Knight, Steven Robertson, Elizabeth Hoile, Olubukola Oladapo, David Francis, Caroline Free

Abstract

Recruitment is a major challenge for many trials; just over half reach their targets and almost a third resort to grant extensions. The economic and societal implications of this shortcoming are significant. Yet, we have a limited understanding of the processes that increase the probability that recruitment targets will be achieved. Accordingly, there is an urgent need to bring analytical rigour to the task of improving recruitment, thereby increasing the likelihood that trials reach their recruitment targets. This paper presents a conceptual framework that can be used to improve recruitment to clinical trials.

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X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Unknown 81 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 17%
Researcher 14 17%
Student > Bachelor 10 12%
Student > Ph. D. Student 8 10%
Student > Doctoral Student 5 6%
Other 14 17%
Unknown 17 21%
Readers by discipline Count As %
Medicine and Dentistry 17 21%
Business, Management and Accounting 10 12%
Social Sciences 7 9%
Psychology 6 7%
Nursing and Health Professions 5 6%
Other 20 24%
Unknown 17 21%