Abstract
BackgroundA challenge for implementation researchers is to develop principles that could generate testable hypotheses that apply across a range of clinical contexts, thus leading to generalisability of findings. Such principles may be provided by systematically developed theories. The opportunity has arisen to test some of these theoretical principles in the Ontario Printed Educational Materials (OPEM) trial by conducting a sub-trial within the existing trial structure. OPEM is a large factorial cluster-randomised trial evaluating the effects of short directive and long discursive educational messages embedded into informed, an evidence-based newsletter produced in Canada by the Institute for Clinical Evaluative Sciences (ICES) and mailed to all primary care physicians in Ontario. The content of educational messages in the sub-trial will be constructed using both standard methods and methods inspired by psychological theory. The aim of this study is to test the effectiveness of the TheoRY-inspired MEssage ('TRY-ME') compared with the 'standard' message in changing prescribing behaviour.MethodsThe OPEM trial participants randomised to receive the short directive message attached to the outside of informed (an 'outsert') will be sub-randomised to receive either a standard message or a message informed by the theory of planned behaviour (TPB) using a two (long insert or no insert) by three (theory-based outsert or standard outsert or no outsert) design. The messages will relate to prescription of thiazide diuretics as first line drug treatment for hypertension (described in the accompanying protocol, "The Ontario Printed Educational Materials trial"). The short messages will be developed independently by two research teams.The primary outcome is prescription of thiazide diuretics, measured by routinely collected data available within ICES. The study is designed to answer the question, is there any difference in guideline adherence (i.e., thiazide prescription rates) between physicians in the six groups? A process evaluation survey instrument based on the TPB will be administered pre- and post-intervention (described in the accompanying protocol, "Looking inside the black box"). The second research question concerns processes that may underlie observed differences in prescribing behaviour. We expect that effects of the messages on prescribing behaviour will be mediated through changes in physicians' cognitions.Trial registration numberCurrent controlled trial ISRCTN72772651
Highlights
A challenge for implementation researchers is to develop principles that could generate testable hypotheses that apply across a range of clinical contexts, leading to generalisability of findings
One of the challenges for implementation researchers is to develop general principles that could produce testable hypotheses about professional behaviour change that apply across a range of clinical contexts, leading to greater generalisability of research findings
The opportunity has arisen to test some of these theoretical principles in the context of the Ontario Printed Educational Materials (OPEM) Trial
Summary
Ethics approval The TRY-ME project has received ethics approval from the Research Ethics Board, Sunnybrook Campus, University of Toronto (Project identification number 135-2004). Testing the validity of the intended distinction between theory-inspired and standard messages The Aberdeen Health Psychology group, approximately 15 doctoral fellows, post-doctoral fellows and academics at the University of Aberdeen, who are not familiar with the OPEM study or with the way the two messages have been constructed, will be given the two messages and asked to make the following judgements with respect to each: Study participants and setting Participants are the primary care physicians in Ontario who receive the informed newsletter (and are participants in the third replicate of the OPEM trial) [5] Data from this sample will be used to test the first hypothesis (specified in study objective two). If the dependent variable is markedly skewed, we will use generalized linear modelling regression to allow for this
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