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acute stroke, correlation factor, cross-sectional study, population distribution, uncertainty



  1. Ni, Chunping
  2. Peng, Jing
  3. Wei, Yuanyuan
  4. Hua, Yan
  5. Ren, Xiaoran
  6. Su, Xiangni
  7. Shi, Ruijie


ABSTRACT: Background: Uncertainty is a chronic and pervasive source of psychological distress for patients and plays an important role in the rehabilitation of stroke survivors. Little is known about the level and correlates of uncertainty among patients in the acute phase of stroke. Purpose: The purposes of this study were to describe the uncertainty of patients in the acute phase of stroke and to explore characteristics of patients associated with that uncertainty. Methods: A cross-sectional descriptive and correlational study was conducted with a convenience sample of 451 consecutive hospitalized acute stroke patients recruited from the neurology department of 2 general hospitals of China. Uncertainty was measured using Chinese versions of Mishel Uncertainty in Illness Scale for Adults on the fourth day of patients' admission. Results: The patients had moderately high Mishel Uncertainty in Illness Scale for Adults scores (mean [SD], 74.37 [9.22]) in the acute phase of stroke. A total of 95.2% and 2.9% of patients were in moderate and high levels of uncertainty, respectively. The mean (SD) score of ambiguity (3.05 [0.39]) was higher than that of complexity (2.88 [0.52]). Each of the following characteristics was independently associated with greater uncertainty: functional status (P = .000), suffering from other chronic diseases (P = .000), time since the first-ever stroke (P = .000), self-evaluated economic pressure (P = .000), family monthly income (P = .001), educational level (P = .006), and self-evaluated severity of disease (P = .000). Conclusion: Patients experienced persistently, moderately high uncertainty in the acute phase of stroke. Ameliorating uncertainty should be an integral part of the rehabilitation program. Better understanding of uncertainty and its associated characteristics may help nurses identify patients at the highest risk who may benefit from targeted interventions.