IR is no less of a science (or art) than any other type of research and hence must generate credible data. Good research practice can ensure credible data by reducing the risk of obtaining inconclusive results due to uncertainty. Uncertainty arises when the intervention is ineffective or the implementation procedures are unclear.8 Good practices must be enshrined throughout the entire process in order to produce valid, reliable, precise, complete and timely data, which can be used to contribute to improved health care services. This section describes some of the most important research-related good practices. Click on each of the headings below to see details.
IR is a dynamic process that often requires adaptations, flexibility and innovation during the course of execution. Such changes/adaptations to the research process must be well documented, coordinated and monitored to ensure credibility and fidelity.
The following questions should underpin documentation of IR projects:
It is important to be objective when documenting processes, and to report both negative and positive experiences. This will facilitate learning and generate evidence to support previously anecdotal reports. Documentation of the various processes, adaptations, revisions and experiences that occurred and impacted the research will ensure that programme planners and policy-makers do not only receive the results of the study but also fully understand the process by which the results were obtained.
Plans do not always proceed as anticipated in IR projects. Adaptations are frequently required as the execution process proceeds and more information is obtained and understood. Designated procedures (e.g. sampling and data tools) should be reviewed regularly to compare what is happening in practice with the original planned procedure and expected observations, so that any necessary adjustments can be made. Staff training is a critical part of this process and helps to ensure that the procedures are understood and adhered to. Training for all essential procedures should be standardized and targeted to the appropriate staff.
To ensure a continuous learning process, training should be followed by mentoring and/or support supervision activities. Researchers need to ensure that the set procedures are adhered to during training, and use the prescribed materials and most up-to-date versions of the data collection tools and instruments. As with all research, IR carries a possibility of adverse events or unintended consequences arising as a result of the intervention. Adverse events can have a negative impact on the adoption and sustainability of the intervention, particularly when these events occur during the initial stage of implementing the project. Resistance to change, inertia and existing investment in the status quo – coupled with the inherently difficult and complex new task – may affect the adoption of a new practice.
A successful project depends on the technical capacity of the research team, and any identified capacity gaps should be addressed promptly through training, mentoring and/or support supervision. Nonetheless, limited research capacity has been identified as one of the constraints to addressing health care priorities in LMICs.9
Generating appropriate, trustworthy evidence depends on the existence of good research infrastructure. Capacity-strengthening strategies need to focus on the comprehensive needs of institutions, including the overall skills and career development of individual researchers, the development of leadership, governance and administrative systems, and strengthening networks among the research community, both nationally and internationally.
In any research project, a pre-test is usually conducted to check the validity and reliability of a data collection tool. Pre-testing allows the research team to check whether the research instructions and questions are sufficiently clear, context specific, and that adequate time is provided to administer the questionnaire, etc.
Data quality is key to having authentic and robust data. As such, it should be taken seriously. Activities such as staff training, support supervision and data feedback can be used to enhance the quality of data.
Data sharing is becoming mandatory in many fields as a way to ensure transparency, to avoid duplication and also reduce plagiarism. Since IR may involve different institutions/organizations, guidelines for data sharing and ownership should be clearly spelt out at the beginning of the project through formal agreements such as a memoranda of understanding. Data sharing should follow a clear process and can be done between research institutions (though not between individuals).
Communicating IR findings to relevant stakeholders must not wait until the closure of the project. On the contrary, in IR knowledge transfers and translation is an integral part of the research process and takes place throughout the project life cycle. Communication should be through appropriate communication channels, formats and language to targeted audiences. It should be timely and the information should be used to contribute to the improvement of health service delivery. Details are described in the IR related advocacy and communication module of this Toolkit.
Continuous monitoring and feedback should be embedded in the project life cycle and the information generated should be fed back into the health system to inform the process for action. The details are discussed in the Integrating IR in the health system module of this Toolkit.