The report concludes with a call for evaluation of the effectiveness of research-informed teaching practice and for psychology practitioners to share their own such teaching practice. Company limited by guarantee registered in England and Wales no. Search site. Back to Knowledge Hub. Applying Psychology Disciplinary Knowledge to Psychology Teaching and Learning: A review of selected psychological research and theory with implications for teaching practice.
Theories and therapeutic applications
The benefits of working with these two teams taught me plenty and provided me with self-confidence to try new ideas. The response was good as most performers participated in both one to one and group activities.
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I made sure that group work was related to short bite size activities that were in line with what performers wanted to engage in. Examples of topics that performers engaged in ranged from coping with pressure to use of mental skills. All consultations commence with a performance analysis form which feedback is provided. This feedback is relative to strengths and areas to improve.
Based on the profile of each participant an action plan is formed that encourages use of a range of mental skills. Participants are provided with a three-week opportunity to implement the prescribed mental skills and then are invited for a further consultation. For the five 'perceived cause of illness' questions in the CS-SRM responses were dichotomized into scores of five to seven indicating agreement that the cause in question was responsible for URTIs versus anything else indicating disagreement.
These dichotomous variables then were entered as independent variables into the regression. Finally, for predictors that were statistically significant, irrespective of whether or not they came from the same theory, we similarly examined the relationship between predictive and outcome variables.
The postal questionnaire survey ran from mid-April to mid-May Fifty-eight percent were male, they had been qualified for a mean SD of 21 7. More respondents provided usable data on intention than provided usable data on behavioural simulation Both these figures were larger than the number of respondents who agreed to allow us to receive their behaviour data Hence, the numbers included in analyses vary between the outcome measures. The three outcome measures were significantly correlated with each other: for Behaviour and Behavioural Simulation, the Pearson r statistic was 0.
The mean SD number of prescriptions issued was 57 31 per patients. The results of the correlation analyses are shown in Table 1. The results of the theory level analyses are shown in Table 1.
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When the analyses were repeated excluding these respondents, there were no differences from the overall analysis. In response to the five clinical scenarios, the respondents indicated that they would prescribe for a mean SD of 1. The median number of prescriptions issued was one with a range of zero to five. From Table 2 , the constructs which predicted behavioural simulation i. The results of the theory level analyses are shown in Table 2. With the range of possible scores for intention of 3 — 21, the mean SD intention score was 6.
The constructs which predicted behavioural intention were: TPB attitudes, perceived behavioural control; SCT risk perception, outcome expectancy, self-efficacy; OLT anticipated consequences, evidence of habitual behaviour; CS-SRM time cyclical , control by treatment and by doctor , consequences, coherence; and knowledge Table 2. For the Stage Model, the GPs who endorsed that they had 'already decided to change my management of URTIs to try to avoid the use of antibiotics' had a mean SD intention score of 6 2. We have successfully developed and applied psychological theory-based questionnaires that have been able to predict prescribing behaviour and two proxies for behaviour — behavioural simulation and intention.
The management of URTI is a frequent behaviour, and our measure of self-reported habitual behaviour consistently predicted our outcome measures. Looking across our three outcome measures, there are also suggestions that issues of perceived control, risk perception, and attitudes may also be important. The theories individually explained a significant proportion of the variance in our dependent variables, but the aggregated analysis suggested that they were measuring similar phenomena within their own individual structures.
Our measure of habit was consistently identified as important, a finding that was supported by the result of the Stage Model analysis albeit analysed as only two stages which suggested that many GPs had already decided to prescribe fewer antibiotics. Because encouraging the implementation of any evidence-based practice commonly entails various methods of increasing knowledge, knowledge was included as a predictive construct in this study.
The knowledge measure included questions about both how and why antibiotics might be used in the management of URTIs. The number of questions answered correctly was not related to the number of antibiotic prescriptions issued but was related to the behavioural simulation and intention scores.
However, knowledge did not enter into any of the three stepwise regressions, indicating that other constructs are consistently more important and suggesting that behaviour change strategies aimed at changing knowledge alone are unlikely to be successful in this clinical area. The stepwise regression analyses revealed that the main construct driving GPs' management of URTI was habit with additional influence from control, attitudes, and risk perception.
Taken together, the results suggest that GPs have considered this frequently performed behaviour and operate in a predominantly habitual manner backed up by beliefs that support their habit. This is a correlational study, so the causative aspects of the theories remain untested in this population; but it is promising for the utility of applying psychological theory to changing clinical behaviour that the constructs are acting as the theories expect.
These results suggest that an intervention that specifically targets these elements should have the greatest likelihood of success in influencing the implementation of this evidence-based practice.
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We used a range of theories and models in both this and another component [ 25 ] of our larger study [ 24 ]. However, across the two studies of different behaviours URTIs, taking dental radiographs and different clinicians GPs, dentists , different constructs predicted different proportions of the variance in the intention and behaviour. This raises the question of what would be an optimum core set of measures if the aim was to cover most behaviours and clinical groups. Given our current limited understanding, this would have to be the subject of both studies replicating this one and further work examining different combinations of theories and models.
Operationalising the constructs with theoretical purity was a challenge.
The preliminary study revealed that it was difficult to ask clinicians about their control over prescribing antibiotics because they believed that, even if they felt there were barriers to performing the behaviour, ultimately they had total control because they wrote the prescription. In the final questionnaire, this meant some questions had to be worded in terms of not doing the behaviour. There was some concern that not prescribing antibiotics may represent a range of alternative behaviours rather than being just a negative reflection of prescribing antibiotics. OLT and II have usually being used as intervention methods to change behaviour.
However, they both have been able to predict behavioural simulation, and OLT predicted intention and behaviour. The model has previously been used mainly to refer to an individual's perceptions of their clinical condition; we used it to measure a clinician's perception of the condition in general. We had difficulty operationalising this model, and further work is needed to explore how best the model can be applied to clinician's behaviour in respect of their patients.
One of the main strengths of this study is that the primary outcome was behaviour. The inclusion of the self-reported secondary outcomes of behavioural intention and simulation made it possible to examine the relationship between these three measures. This is important because behaviour is usually more difficult and expensive to measure than either of these proxy measures. By virtue of their significant correlation, the results suggest that self-reported measures have the potential to proxy behavioural data when testing an intervention prior to implementation in a service-level trial.
However, although the two proxy measures intention and simulation were moderately correlated, the correlation between either and behaviour was weak. It is possible that the proxy measures are poor predictors of behaviour, though it is important to remember that the models we have used are focussing on modifiable behaviour. This cannot be quantified in our predictive study design but will only ever be a small proportion of behaviour.
However, it is also important to consider the validity of our behaviour measure. This suggests that our operationalisation of the models was good, but that either the models do not work for this behaviour in GPs or there are problems with our measure of behaviour, or both. A systematic review [ 39 ] found only 10 studies exploring the relationship between intention and behaviour in healthcare professionals, but these reported explaining a similar proportion of the variance in observed behaviour to the studies in Armitage and Connor's review [ 38 ].
This suggests that the problem is with our measure of behaviour. There could be two problems — prescribing data may not be a good proxy for the behaviour as we asked about it in our questionnaires, or there may be biases within prescribing data. We have already identified the potential problem with using antibiotic prescription as a proxy for the management of patients with URTIs without prescribing antibiotics.
Not only may 'prescribing' not be the reverse of 'not prescribing', 'not prescribing' may represent a number of alternate behaviours. Prescribing data was chosen because it was available from routine data sources, and was therefore inexpensive to collect. Antibiotic prescribing was chosen because it was more likely that a prescription for an acute illness as opposed to a chronic illness managed through a repeat prescribing system would be attributed to the GP who issued it.
Scotland was chosen because the most commonly used computer system was likely to ascribe an antibiotic prescription to the issuing doctor. Despite this, we know that there are errors in the attribution of prescriptions to doctors, with 45 respondents reporting that prescriptions had frequently been written in their name by someone else. Finally, our standardisation by the number of patients registered with the GP assumes that each doctor has the same presentation rate. We sought to minimise variation in this by measuring over a month period, but it is possible that this was still a problem.
In future studies of this kind it will be important to invest more in the measurement of the behavioural data. Our final response rate was not high compared to what would be expected for a postal questionnaire survey. Cummings et al. Kaner et al. Since these three studies, day-to-day work pressures in UK NHS primary care have continued to rise, and our operationalisation of multiple models resulted in a long questionnaire asking seemingly repetitive questions. Additionally, our request to access behavioural data deterred 31 respondents who returned a completed questionnaire; it may have deterred a larger group from even completing a questionnaire.
Although we cannot make direct comparisons, our respondents appear well-matched with the overall population of Scottish GPs on gender, age and prescribing rates but came from larger practices and were more likely to be trainers.
Mean national rates of antibiotic prescribing in having made, where possible, similar exclusions to those made in this study was 65 prescriptions per patients. Therefore, while we should be cautious about generalising from our respondents to the population of Scottish GPs, this is less of an issue at this exploratory stage of using these methods. Our aim was not to generate data that was representative but to receive our pre-specified number of responses from a population who had a range of behaviour, reported a range of behavioural simulation and intention, and who reported a range of cognitions.
The study achieved this aim. This study provides evidence that psychological models can be useful in understanding and predicting clinical behaviour. Taking a theory-based approach enables the creation of a replicable methodology for identifying factors which predict clinical behaviour.
However, there remain conceptual challenges in operationalising a number of the models and a range of methodological challenges in terms of instrument development and measurement of behaviour that have to be surmounted before these methods could be regarded as routine. Milbank Q. Grol R: Improving the quality of medical care. Building bridges among professional pride, payer profit, and patient satisfaction. Fourth national study Fam Pract. Bradley CP: Uncomfortable prescribing decisions: a critical incident study.
Cockburn J, Pitt S: Prescribing behaviour in clinical practice: patients' expectations and doctors' perceptions of patients' expectations - a questionnaire study. Cochrane Database of Systematic Reviews Issue 1. Edited by: Collaboration C.