Glossary: C
Causal loop. A causal loop is a circular chain of variables affecting one another in turn. So one variable would affect a second variable and so on. The last variable in the loop would affect the first variable.
Causal loop diagrams: Causal loop diagrams can be used to show the relationships between causal factors and how they operate within a system (or systems).
Causality: Cause is what makes something else happen; effect refers to what results. Cause is the why something happened and effect is the what happened. In intervention development we often refer to effect as the outcome or outcomes.
Change mechanism: a lever which triggers a sequence of outcomes in an outcomes chain.
Citizen science: encompasses the active participation of members of the public in research. Participation can range from collecting data to all aspects of the research process, including generation of research questions, data collection, analysis, evaluation and reporting.
Cohort: A group of people sharing a common demographic experience. The most common cohort is a group of people born in the same year (birth cohort), but there are numerous other examples, such as those who, in the same period of time, married (marriage cohorts), or migrated (migration cohorts).
Community empowerment: A process where people work together to make change happen in their communities by having more power and influence over what matters to them.
Community engagement: A way of developing a working relationship between public bodies (e.g. local councils) and community groups. Good community engagement means that both groups can understand and act on the needs or issues of community experiences, helping to achieve positive change.
Community: A general term referring to the people who are connected by a common interest including geography (living in a locality or to the locality itself) or a shared interest.
Complex interventions: May have multiple, interacting components and non-linear causal pathways, with variability in the content, context and mode of delivery, as well as the unpredictability of their effect on outcomes.
Complex systems: systems that provide the conditions that allow complexity to arise (e.g., feedbacks, non-equilibrium, lack of central control, heterogeneity and/ numerosity of its constituents elements) and, as result, present one or more of the following features: spontaneous order or self-organization, nonlinearity, robustness, nested structure, history and memory, and adaptive behaviour.
Complexity: A state of being complex, confusing or entangled.
Confidence Interval: A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable. The specified probability is called the confidence level, and the end points of the confidence interval are called the confidence limits.
Confounder: A factor that is associated with both an intervention and the outcome of interest. For example, if people in the experimental group of a controlled trial are younger than those in the control group, it will be difficult to decide whether a lower risk of death in one group is due to the intervention or the difference in age. Age is then said to be a confounder, or a confounding variable. Randomisation is used to minimise imbalances in confounding variables between experimental and control groups. Confounding is a major concern in non-randomised trials.
Construct: Component part of theory.
Co-production: An approach in which researchers, practitioners and the public work together, sharing power and responsibility from the start to the end of the project, including the generation of knowledge.
Cost-effectiveness: Whether the intervention can be effective at affordable costs.
