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dc.contributor.authorEvans, Chris
dc.date.accessioned2017-09-29T14:20:13Z
dc.date.available2017-09-29T14:20:13Z
dc.date.issued2005
dc.identifier.citationLutz, W., Leach, C., Barkham, M., Lucock, M., Stiles, W. B., Evans, C., Noble, R. & Iveson, S. (2005). Predicting change for individual psychotherapy clients on the basis of their nearest neighbors. Journal of Consulting and Clinical Psychology, 73 (5), pp.904-913.
dc.identifier.other10.1037/0022-006x.73.5.904
dc.identifier.urihttp://hdl.handle.net/20.500.12904/9328
dc.description.abstractThis study extended client-focused research by using the nearest neighbor (NN) approach, a client-specific sampling and prediction strategy derived from research on alpine avalanches. Psychotherapy clients (N = 203) seen in routine practice settings in the United Kingdom completed a battery of intake measures and then completed symptom intensity ratings before each session. Forecasts of each client's rate of change and session-by-session variability were computed on the basis of that client's NNs (n = 10-50 in different comparisons). Alternative forecasts used linear or log-linear slopes and were compared with an alternative prediction strategy. Results showed that the NN approach was superior to the alternative model in predicting rate of change, though the advantage was less clear for predicting variability.
dc.description.urihttp://psycnet.apa.org/journals/ccp/73/5/904/
dc.subjectAnxiety
dc.subjectBehaviour therapy
dc.subjectCommunity mental health service
dc.subjectDepression
dc.subjectPatient-centred care
dc.subjectPsychology
dc.subjectResidence characteristics
dc.titlePredicting change for individual psychotherapy clients on the basis of their nearest neighbors
dc.typeArticle
html.description.abstractThis study extended client-focused research by using the nearest neighbor (NN) approach, a client-specific sampling and prediction strategy derived from research on alpine avalanches. Psychotherapy clients (N = 203) seen in routine practice settings in the United Kingdom completed a battery of intake measures and then completed symptom intensity ratings before each session. Forecasts of each client's rate of change and session-by-session variability were computed on the basis of that client's NNs (n = 10-50 in different comparisons). Alternative forecasts used linear or log-linear slopes and were compared with an alternative prediction strategy. Results showed that the NN approach was superior to the alternative model in predicting rate of change, though the advantage was less clear for predicting variability.


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