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健康素养、计算能力与图形化乳腺癌风险估计的解读。

Health literacy, numeracy, and interpretation of graphical breast cancer risk estimates.

机构信息

Saddleback Memorial Medical Center, USA.

出版信息

Patient Educ Couns. 2011 Apr;83(1):92-8. doi: 10.1016/j.pec.2010.04.027.

Abstract

OBJECTIVE

Health literacy and numeracy are necessary to understand health information and to make informed medical decisions. This study explored the relationships among health literacy, numeracy, and ability to accurately interpret graphical representations of breast cancer risk.

METHODS

Participants (N=120) were recruited from the Facing Our Risk of Cancer Empowered (FORCE) membership. Health literacy and numeracy were assessed. Participants interpreted graphs depicting breast cancer risk, made hypothetical treatment decisions, and rated preference of graphs.

RESULTS

Most participants were Caucasian (98%) and had completed at least one year of college (93%). Fifty-two percent had breast cancer, 86% had a family history of breast cancer, and 57% had a deleterious BRCA gene mutation. Mean health literacy score was 65/66; mean numeracy score was 4/6; and mean graphicacy score was 9/12. Education and numeracy were significantly associated with accurate graph interpretation (r=0.42, p<0.001 and r=0.65, p<0.001, respectively). However, after adjusting for numeracy in multivariate linear regression, education added little to the prediction of graphicacy (r(2)=0.41 versus 0.42, respectively).

CONCLUSION

In our highly health-literate population, numeracy was predictive of graphicacy.

PRACTICE IMPLICATIONS

Effective risk communication strategies should consider the impact of numeracy on graphicacy and patient understanding.

摘要

目的

健康素养和计算能力对于理解健康信息和做出明智的医疗决策是必要的。本研究探讨了健康素养、计算能力与准确解读乳腺癌风险图形表示之间的关系。

方法

参与者(N=120)从 Facing Our Risk of Cancer Empowered(FORCE)会员中招募。评估健康素养和计算能力。参与者解读了描述乳腺癌风险的图形,做出了假设的治疗决策,并对图形的偏好进行了评分。

结果

大多数参与者为白种人(98%),至少完成了一年的大学学业(93%)。52%的参与者患有乳腺癌,86%的参与者有乳腺癌家族史,57%的参与者有有害的 BRCA 基因突变。平均健康素养得分为 65/66;平均计算能力得分为 4/6;平均图形能力得分为 9/12。教育程度和计算能力与准确解读图形显著相关(r=0.42,p<0.001 和 r=0.65,p<0.001)。然而,在多元线性回归中调整计算能力后,教育对图形能力的预测作用不大(r(2)=0.41 与 0.42 相比,分别)。

结论

在我们这个健康素养较高的人群中,计算能力可以预测图形能力。

实践意义

有效的风险沟通策略应考虑计算能力对图形能力和患者理解的影响。

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