Across all health care settings, certain patients are perceived as ‘difficult’ by clinicians. This paper’s aim is to understand how certain patients come to be perceived and labelled as ‘difficult’ patients in community mental health care, through mixed-methods research in The Netherlands between June 2006 and October 2009. A literature review, a Delphi-study among experts, a survey study among professionals, a Grounded Theory interview study among ‘difficult’ patients, and three case studies of ‘difficult’ patients were undertaken. Analysis of the results of these qualitative and quantitative studies took place within the concept of the sick role, and resulted in the construction of a tentative explanatory model. The ‘difficult’ patient-label is associated with professional pessimism, passive treatment and possible discharge or referral out of care. The label is given by professionals when certain patient characteristics are present and a specific causal attribution (psychological, social or moral versus neurobiological) about the patient’s behaviours is made. The status of ‘difficult’ patient is easily reinforced by subsequent patient and professional behaviour, turning initial unusual help-seeking behaviour into ‘difficult’ or ineffective chronic illness behaviour, and ineffective professional behaviour. These findings illustrate that the course of mental illness, or at least the course of patients’ contact with mental health professionals and services, is determined by patient and professional and reinforced by the social and mental health care system. This model adds to the broader sick role concept a micro-perspective in which attribution and learning principles are incorporated. On a practical level, it implies that professionals need to look into their own role in the perpetuation of difficult behaviours as described here.
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This article presents the life stories of four older women in Vienna in order to better understand the role of occupation in the course of ageing. A qualitative life-story method in the narrative tradition was used as a design of this multiple case study. The stories presented extend beyond an illness or deficit narrative and contribute to a more multifaceted narrative of the subjective experience of ageing in occupational terms in connection with identity. The women did not perceive themselves as old or sick despite problems in mobility, the presence of chronic disease and advanced age. This was associated with their engagement in occupation that was meaningful and linked to their identity. Engaging occupation is the means to continue, test, and adapt to the ageing self. Because occupation is like a litmus-test of one's identity and capacities, the women used it as a measure of change while ageing. Using Atchley's continuity theory, the attempt of the four older women to maintain a balance between adapting and struggling to continue their occupations is discussed in relation to their identity. The results expand Atchley's continuity theory by adding an occupational perspective.
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In the literature about web survey methodology, significant e orts have been made to understand the role of time-invariant factors (e.g. gender, education and marital status) in (non-)response mechanisms. Time-invariant factors alone, however, cannot account for most variations in (non-)responses, especially fluctuations of response rates over time. This observation inspires us to investigate the counterpart of time-invariant factors, namely time-varying factors and the potential role they play in web survey (non-)response. Specifically, we study the effects of time, weather and societal trends (derived from Google Trends data) on the daily (non-)response patterns of the 2016 and 2017 Dutch Health Surveys. Using discrete-time survival analysis, we find, among others, that weekends, holidays, pleasant weather, disease outbreaks and terrorism salience are associated with fewer responses. Furthermore, we show that using these variables alone achieves satisfactory prediction accuracy of both daily and cumulative response rates when the trained model is applied to future unseen data. This approach has the further benefit of requiring only non-personal contextual information and thus involving no privacy issues. We discuss the implications of the study for survey research and data collection.
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