2010
DOI: 10.1177/0306624x10389435
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No-Show at a Forensic Psychiatric Outpatient Clinic

Abstract: Nonattendance for and late cancellations of scheduled appointments, that is no-show, is a well-known phenomenon in psychiatric outpatient clinics. Research on the topic of no-show for initial and consecutive appointments in the field of forensic psychiatry is scarce. This study therefore aims to determine the prevalence and causes of no-show and to explore reasons for nonattendance. The study was carried out in an outpatient clinic in northern Netherlands. Telephone interviews were administered to 27 no-show c… Show more

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Cited by 15 publications
(21 citation statements)
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“…One of the key tasks of building an accurate no-show predictive model is to study the contributing factors. Prior work has shown that factors can include gender, age [40,41], social factors [42], patient perceptions of medical providers, direct patient costs, distance to the clinic, a lack of a personal relationship with the physicians [6], adherence to physician visits [43], the perception of long waiting times [44], the delay in scheduling appointments [45], the long lead time from scheduling Research Article date to appointment date [46], and the provider's specialty [6]. Others estimated the no-show rate based on factors, such as purpose of visit, day of the week, and age using data mining techniques [47,48] or logistic regression modeling [49,50] and developed a predictive model for the probability of no-shows for groups of patients that shared common attributes, often in an attempt to minimize costs rather than predicting errors.…”
Section: Introductionmentioning
confidence: 99%
“…One of the key tasks of building an accurate no-show predictive model is to study the contributing factors. Prior work has shown that factors can include gender, age [40,41], social factors [42], patient perceptions of medical providers, direct patient costs, distance to the clinic, a lack of a personal relationship with the physicians [6], adherence to physician visits [43], the perception of long waiting times [44], the delay in scheduling appointments [45], the long lead time from scheduling Research Article date to appointment date [46], and the provider's specialty [6]. Others estimated the no-show rate based on factors, such as purpose of visit, day of the week, and age using data mining techniques [47,48] or logistic regression modeling [49,50] and developed a predictive model for the probability of no-shows for groups of patients that shared common attributes, often in an attempt to minimize costs rather than predicting errors.…”
Section: Introductionmentioning
confidence: 99%
“…Similarly, forensic patients in community settings often experience problems in psychosocial and occupational functioning (Feitsma, Popping, & Jansen, 2010;Henrichs, Bogaerts, Sijtsema, & Klerx-van Mierlo, 2014). In our samples, the rewards increasing eudaimonic well-being were rated as the most valuable, which suggests that forensic patients are willing to provide the effort required to achieve these goals.…”
Section: Discussionmentioning
confidence: 64%
“…This percentage of contact and acceptance to participate can be related to the 72‐hour window of the scheduled appointment that we predefined in order to avoid recall bias. In other similar studies, between 15.9% and 69.4% of patients were contacted and included . Patients not included have a higher percentage of males and are older as in the study by Pereira‐Victorio et al The low response rate and the differences between respondents and nonrespondents are consistently observed in most similar publications on causes of absenteeism …”
Section: Discussionmentioning
confidence: 88%
“…Although there are cultural and regional differences between the different studies that evaluated the causes of nonattendance in adult outpatient scheduled visits, consistently having forgotten about the appointment is the main cause. The frequency of this cause is variable in different studies: between 22.2% and 44% …”
Section: Discussionmentioning
confidence: 99%
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