School of Nursing, Vanderbilt University, Nashville, TN 37240, USA.
Nurs Res. 2010 Mar-Apr;59(2):140-6. doi: 10.1097/NNR.0b013e3181d1a6f6.
Efficient measures are needed to identify individuals at risk for depression to optimize intervention efforts that can enhance health-related outcomes.
The purpose of this study was to examine the effectiveness of the six-item Psychological Vulnerability Scale (PVS) as a predictor of depressive symptoms and change in depressive symptoms while controlling for two established predictors of depression-arthritis helplessness (AHI) and functional impairment (FI).
Data from 125 patients with rheumatoid arthritis (73% women) were used in hierarchical regression analyses to examine whether the PVS could be used to predict unique variance in depressive symptoms (Center for Epidemiological Studies-Depression [CES-D]) cross sectionally (N = 125) and change in depressive symptoms (N = 93).
The three-predictor cross-sectional model was highly significant, F(3, 121) = 25.6; p < .001, explaining 39% of the variance in CES-D scores assessed at the same point in time. Controlling for both AHI and FI, the PVS explained an additional 9.3% of the variance in CES-D scores. To examine changes in CES-D scores over a 1-year period, CES-D scores at the later time were regressed on CES-D scores from a year earlier. On the next two steps, AHI, FI, and PVS scores assessed a year earlier were entered into the model. The full model predicted 56% of the variance in depressive symptoms, F(4, 88) = 27.7, p < .001, with the PVS accounting for a unique 5.6% of the variance in change in CES-D scores.
Because of its brevity, the PVS can be an efficient screening tool for individuals at risk for depression. More research is needed to substantiate the value of the PVS for identifying individuals who could benefit from interventions designed to prevent depression.
需要有效的措施来识别有抑郁风险的个体,以优化干预措施,从而改善健康相关结果。
本研究旨在检验六项目心理脆弱性量表(PVS)作为抑郁症状预测指标的有效性,并控制两个已确立的抑郁预测指标——关节炎无助感(AHI)和功能障碍(FI),同时检验其对抑郁症状变化的预测能力。
使用来自 125 例类风湿关节炎患者(73%为女性)的数据,进行分层回归分析,以检验 PVS 是否可以用于预测横断面(N=125)和抑郁症状变化(N=93)时的抑郁症状(流行病学研究中心抑郁量表 [CES-D])的独特方差。
三预测因子的横断面模型具有高度显著性,F(3,121)=25.6;p<0.001,解释了 CES-D 评分在同一时间点评估的 39%的方差。在控制 AHI 和 FI 后,PVS 解释了 CES-D 评分方差的另外 9.3%。为了检验 CES-D 评分在 1 年内的变化,将较晚时间的 CES-D 评分回归到前一年的 CES-D 评分。在下两个步骤中,将前一年评估的 AHI、FI 和 PVS 分数输入模型。完整模型预测了抑郁症状的 56%,F(4,88)=27.7,p<0.001,PVS 解释了 CES-D 评分变化的独特 5.6%。
由于其简洁性,PVS 可以作为评估抑郁风险个体的有效筛查工具。需要进一步的研究来证实 PVS 对识别可能受益于预防抑郁干预措施的个体的价值。