The methods of the studies (along with the questionnaires used) are described in detail elsewhere
]. Four of the analyses have been previously reported
]; we report the fifth analysis in Additional file
and include the questionnaire used as Additional file
In summary, these were five predictive studies exploring theory-based cognitions as predictors across five different clinical behaviors within a series of random samples of primary care dentists and primary care physicians in Grampian, Tayside and Lothian in Scotland and Durham, Newcastle and South Tees in northern England. Data on theory-based cognitions (explanatory measures) and two interim outcome measures (stated behavioral intention and simulated behavior) were collected by a single postal questionnaire survey that took place during the 12-month period to which data on objective measures of behavior (collected from routine administrative sources) related.
Planned analyses explored the predictive value of theories and theory-based cognitions in explaining variance in the behavioral data.
Explanatory measures and theories
Explanatory measures were chosen to reflect both volitional (processes that are aware, deliberative, reflective, and may therefore be considered subject to volition) and non-volitional (processes that are associative, automatic, impulsive, and may therefore be considered not subject to volition; here represented by Learning Theory, or LT) theoretical constructs that have been found to predict behavior. All measures were included in each study. Measures were derived for the constructs in the Theory of Planned Behavior (TPB)
], Social Cognitive Theory (SCT)
], and for illness representations specified by the Common Sense Self Regulation Model (CSSRM)
]. We constructed self-report measures of two constructs from LT (habitual behavior and anticipated consequences)
], a measure of implementation intentions (II)
], and questions to assess stage in the process of change as specified by the Precaution Adoption Process (PAP)
] by Aizen and Fishbein proposes that the strength of an individual’s behavioral intention to engage in a behavior, and the degree of control they feel they have over that behavior (perceived behavioral control) are the proximal determinants of engaging in it. The perceived behavioral control construct is closely related to the concept of self-efficacy in SCT. The theory of planned behavior also proposes that behavioral intention strength is determined by three variables: attitudes towards the behavior (the extent to which the behavior will result in consequences that the person values); subjective norms for the behavior (the belief that other people whose opinion influences the person think the person should engage in the behavior); and perceived behavioral control over it. These variables in turn are based upon salient behavioral, normative, and control beliefs about the behavior. Each predictor variable may be measured directly (by asking respondents about their overall attitude), or indirectly (by asking respondents about specific behavioral beliefs and outcome evaluations). Aizen suggests that behavior change is brought about by interventions that change these beliefs.
] proposes that behavior is determined by three kinds of expectancies: situation-outcome expectancies, outcome expectancies, and self-efficacy expectancies. Situation-outcome expectancies are beliefs about how events are connected (e.g., a diagnosis of cancer is likely to result in death). Outcome expectancies refer to beliefs about the consequences of performing a behavior (e.g., if I stop smoking, I will put on weight). Self-efficacy expectancies are beliefs about one’s ability to perform the behavior (e.g., I can stop smoking). Self-efficacy has been found to be the strongest determinant of behavior, and the theory proposes four methods of changing self-efficacy in order to change behavior: providing mastery experiences (i.e., successful behavioral performance); modelling (i.e., observation of successful behavioral performance); verbal persuasion; and addressing attributions for physiological and affective states
] proposes that individuals attempt to make sense of illness by making use of pre-existing knowledge or schemas to develop both cognitive representations of the illness (including its identity, consequences, timeline, cause, and cure/control) and emotional representations of the threat involved. These representations give rise to behavioral responses. For patients, behavioral responses may include going to see a doctor, taking prescribed or non-prescribed medicines, or participating in rehabilitation programmes. For clinicians, responses may include referring a patient for diagnostic tests, prescribing a drug, or restoring a carious tooth. The model is described as self-regulatory because this is seen as a dynamic process. The aim of the actions that people take in response to a health threat is to reduce the degree of threat or to restore their own (or their patient’s) emotional equilibrium. The individual’s own understanding of the health condition and their personal emotional reaction are central to the self-regulatory approach. It seems likely that health professionals also experience emotional reactions to some conditions (e.g., terminal conditions in children) or patient groups, for example ‘heart sink’ reactions, which may influence their practice. In this model, behavior change results from changes in the cognitive representations or in the responses available to manage either the threat or the emotions.
Learning theory proposes that behaviors that have positive consequences for the individual (such as remuneration) are likely to be repeated, whereas those that have unpleasant consequences will become less frequent
]. Learning happens when consequences occur if and only if the behavior has occurred and can take a variety of forms, from material rewards (e.g., financial incentives), through social rewards (e.g., receiving praise) to personally salient rewards (e.g., achieving a desired goal). The principle that positive consequences promote repetition of behavior is well established and has been widely and successfully used to understand behavior and behavior change
]. As behaviors are repeatedly rewarded, they may become ‘habitual’ and, if reward is scheduled appropriately, the behavior may be maintained with a reduced frequency of reward; if a behavior is increased by being rewarded and then removed without planned scheduling, the learned behavior will be ‘extinguished’ unless some other form of reward is available,
‘Implementation Intentions’ are explicit ‘if-then’ action plans about when and where a goal intention will be achieved
] has made the distinction between goal intentions and II. A goal intention is an intention to perform a behavior or achieve a goal (e.g., I intend to reduce the number of referrals I make for lumbar spine x-rays). This is conceptually close to the behavioral intention construct in the TPB. Gollwitzer argues that by creating an II, people effectively transfer control of the behavior to the environment—establishing cues to action. For example, by saying that ‘When a patient tells me about their low back pain, I will explain the pros and cons of an x-ray to them.’
Stage theories propose that behavior change occurs in a stepwise process, rather than a linear fashion as implied by motivational or action theories. From a stage theory perspective, interventions to facilitate change will be most effective if they are tailored to the stage an individual has reached within this process. While there are differences between the stage models in the number and nature of stages proposed, stage theories typically distinguish motivation and action steps
]. In the PAP model, Weinstein has proposed an additional early stage when individuals may be unaware of the need for behavior change, a stage which may have particular relevance to the early stages of the implementation of evidence
A common implicit model is the Knowledge-Attitudes-Behavior model. This assumes that a change in knowledge will produce a change in attitude, and this will, in turn, produce a change in behavior. While this model has some value, it should be tested rather than assumed before being used as a basis for behavior change. Accordingly, we examined the ability of knowledge to predict behavior.
The questions were developed based on the standard methods for each theory where possible. Additionally, for each behavior five knowledge questions, for which there was good evidence, were developed by the study team. Table
provides a summary of the explanatory measures used in each of the studies (with examples from the Managing Low Back Pain Without Ordering Lumbar Spine X-rays study). Questions were rated on a seven-point Likert scale from ‘strongly disagree’ to ‘strongly agree.’ Scores were adjusted so that a higher score equated with a greater propensity towards evidence-based behavior. Exemplar questionnaires are available through previous publications
] and in Additional file
Summary of the explanatory measures
Behaviors, simulated behavior, and behavioral intention (dependent variables)
The three dental and two medical behaviors are shown in Table
. In each case, the description of the clinical behavior contained in the questionnaires was specified according to the TACT principle—in terms of its Target patient, the Action to be performed, the Context or conditions for the action and when the action would be performed (Time). For example, for managing low back pain, the target is patients presenting with back pain, the action is managing the patient without referring for lumbo-sacral spine (lumbar) X-rays, the context is general practice, and the time is (implicitly) now.
Description of the five study behaviors, definition and mean (SD) rates of performing the objective measures of these behaviors, mean (SD) simulated behavior scores and mean (SD) intention scores
For each of the five behaviors, we collected two measures of behavior, an objective measure (Table
) collected from routine data systems and gathered for a 12-month period to control for seasonal variations, and a measure of simulated behavior. For simulated behavior, key elements that may influence clinicians’ decisions to perform the behaviors were identified from the literature, opinion of the clinical members of the research team, and the interviews with clinicians. From this, we constructed five clinical scenarios that described patients presenting in relation to the index behaviors. Respondents were asked to decide what they would do, and responses were summed to create a total score out of a possible maximum of five (Table
We also elicited behavioral intention using three questions assessing intention to perform the behavior of interest. With a behavior specific stem the questions were worded ‘I have in mind to,’ ‘I intend to,’ ‘I aim to’ (rated on a seven-point scale from ‘strongly disagree’ to ‘strongly agree’).
Clinicians were sent an invitation pack comprising a letter of invitation, a questionnaire consisting of theory-based cognition measures (explanatory measures), two interim outcome measures (behavioral intention and behavioral simulation), and demographic measures, plus a consent form to allow access to their data (objective behavior measure) from the routine systems, and a reply-paid envelope. Three postal reminders were sent to non-responders at two, four, and six weeks from the first mailing.
Each study included a large number of predictor variables, and a power calculation suggested that, for each of the five studies, a minimum sample of 200 clinicians was required to detect an effect size of 0.40, with alpha of 0.05, 95% power.
The measures of the theoretical constructs were tested for acceptable internal consistency. If the criterion value (0.60) was not reached, items were dropped from the variable measures until the maximum possible Cronbach’s alpha was achieved or, for two items, a correlation greater than 0.25.
We examined the relationship between predictive and dependent variables using correlation, except for the PAP where ANOVA was used. For the two general medical behaviors for the PAP analysis, respondents were dichotomized into two groups (decided to perform desired behavior or have already performed desired behavior versus all other responses), and the relationship between predictive and outcome variables were examined using regression models.
Multiple regression analyses were used to examine the predictive value of each of the theoretical models. For the behavior ‘managing low back pain without referring for a lumbar spine x-ray,’ given the distribution of the behavioral data, we used negative binomial regression (NBR) to model the predictive ability of individual theoretical constructs and complete theories.
The study was approved by the UK South East Multi-Centre Research Ethics Committee (MREC/03/01/03).