助推手卫生的行为干预策略

保持手卫生是感染防控的重要策略, 但是如何提高其依从性成为预防传染性疾病和减少医疗机构获得性感染的一大挑战。以行为科学为基础的手卫生助推干预以更“隐性”的方式将洗手转变为一种可自动触发的行为习惯, 弥补了以知识分享和健康宣教为主的传统手卫生干预策略的诸多局限性。基于影响机制的不同, 手卫生助推干预策略可分为提供决策信息、优化决策选项、影响决策结构、提醒决策方向4个大类的框架体系。多模式助推策略的有效性也已在实践中得到印证, 但目前还非常缺乏在中国社会文化情境下开展的助推洗手行为的干预研究, 今后可尝试基于行为科学理论有针对性地在医院、学校和社区等公共场所开展此类干预研究和实践, 为感染防控、疾病预防和改善公共健康做出相应的贡献。     

助推戒烟的行为干预策略

烟草危害是人类所面临的重大公共健康挑战之一。传统的控烟策略主要依赖健康宣教、烟草税和控烟条例等手段,但是较少考虑到吸烟行为的“非理性因素”及其心理机制,因此仍不能有效帮助吸烟者成功戒烟。行为科学的发展为助推吸烟者成功戒烟提供了新视角。以实施主体和干预的影响机制为划分标准,可将助推戒烟的行为干预策略分为由政府和公共健康服务部门执行的情境型干预策略和认知型干预策略以及吸烟者可自主执行的情境型和认知型干预策略,一方面便于吸烟者和相关部门选取可执行的戒烟行为干预策略,另一方面为进一步开发和验证助推戒烟的行为干预策略提供参考。虽然基于行为科学的助推干预策略已经取得了一定的进展,未来研究仍有必要进一步在真实世界中验证和评估戒烟助推干预策略的效果。未来也有必要进一步将行为改变技术融入到数字化的戒烟服务或应用小程序中去以提升其效果。未来也需要考察如何应用戒烟助推干预策略防止电子烟产品的负面影响并减少这类策略的误用和滥用。     

健康领域的跨期决策与健康行为

健康领域的跨期决策关系着个体和国民的健康和福祉。目前学界对该领域的研究主要停留在参考传统金钱领域的相关理论模型和方法的阶段, 但健康跨期决策具有领域特异性, 沿袭金钱领域理论模型和方法, 导致该领域在研究方法和结果上存在较大的不一致性。健康跨期决策的行为后果是该领域关注重点, 多数研究均报告个体的低时间折扣率、高未来时间取向与其健康保护行为正相关, 与健康风险行为呈负相关。该领域也关注健康跨期决策的影响机制, 如决策对象和决策主体的核心特征等因素。未来研究亟需发展适用于健康领域的跨期决策模型和研究范式, 明确健康行为与跨期决策偏好的关系, 深入探讨健康跨期决策的内在选择机制, 并在健康行为干预和医疗卫生政策应用方面进行更多的尝试和探索。     

“红绿灯”营养信息标签助推低自控者健康决策

采用问卷调查和行为实验相结合的方法,探究自我控制水平对健康决策的影响,并进一步探讨“红绿灯”营养信息标签在其中的助推作用。结果发现,自我控制对健康决策有显著的正向预测作用,同时,“红绿灯”营养信息标签可以有效帮助低自控者做出健康决策。本研究结果不仅对以往相关理论做了有效补充,也对助推低自控者进行健康决策具有指导意义。     

基于助推的环保行为干预策略

近年来, “助推”被越来越多地用于气候变化与生态环境治理实践, 成为促进人们环保行为的有效策略。但目前国内基于助推思想的环保行为干预研究和实践探索都还很缺乏。环保行为需要助推的根本原因在于人们在环保领域的决策存在认知局限和动机不足。相应的, 在认知和动机两大视角下主要有7种环保行为助推策略。认知视角的助推策略包括默认选项、框架效应和示范性规范, 动机视角的助推策略旨在激发家国动机、遗产动机、获益动机和自主动机以促进人们的环保行为。助推环保行为在实践中也存在争议, 实践工作者需明确助推环保行为的外部环境与传统社会治理思路的关系, 并掌握选择架构设计的技术要点。     

CSF3R和行动控制对应激与健康饮食关系的调节作用：应激影响健康行为的个体化模型的初步证据

应激在饮食方面的反应因人而异;有些人在应激下喜欢多吃甜食和其他高热量食物,而另一些人则没有这样的倾向。这可能与两种类型的迷走神经系统功能优势性有关：向营养型或兴奋型。本研究以14675位中国成年人为样本,结合健康行动过程模型和迷走神经支配性理论,发现应激显著预测了健康饮食意向的减少,以及随后健康饮食行为的减少。CSF3R基因rs4076431及其连锁位点rs4498771, rs10752589, rs9660229调节了应激与健康饮食意向之间的关系,而行动控制调节了健康饮食意向与健康饮食行为之间的关系。以rs4076431为例,应激与健康饮食意向之间的负向关系在AA基因型(向营养型)中比在G等位基因携带者(兴奋型)中更强。行动控制分数越高,健康饮食行为越多,而且该行为越少受到应激和健康饮食意向的影响。基于这些结果我们提出了应激影响健康行为的个体化模型,应激下的健康管理应考虑生理、行为、情境等多个层面的影响因素。     

红色及图形表征对健康行为的助推作用

本研究旨在通过3个实验从信息凸显的角度探讨红色图形对健康行为的助推作用。实验一和实验二分别采用星号图和直条图，通过操纵图形颜色或装有实验材料的信封颜色，以探讨数量信息表征方式（图形 vs. 数字）及颜色（红色 vs. 蓝色、黑色或白色）对健康风险回避行为的影响。实验三将针对宫颈癌疫苗接种这一健康行为，采用更加形象的人物杆形图进一步考察在不同决策框架下（损失/收益）红色图形能否有效助推个体的健康行为意愿。结果发现，在损失及收益情景下，红色图形均能稳定助推个体的健康行为，但当用信封颜色操纵颜色背景时，红色的影响力会降低。结果支持：红色图形可有效助推个体的健康行为。     

“长计远虑”的助推效应:怀孕与环境跨期决策

如何做出有利于长期环境利益的跨期决策是一个重要的科学问题。4个研究探讨了为子孙后代的"长计远虑"对环境跨期决策的助推作用。研究1通过对比真实孕妇和未孕女性,探讨怀孕、长计远虑水平和环境跨期决策三者之间的关系;研究2通过启动育龄女性的怀孕状态,进一步验证了长计远虑在怀孕与环境跨期决策中的中介作用;研究3和4开发助推研究范式,探讨为子孙后代的长计远虑对环保政策支持意愿和实际环保行为的影响。结果发现:不管是真实怀孕还是实验室启动怀孕,都会提高人们的长计远虑水平,并且在环境跨期决策中偏好远期选项;将长计远虑作为一种助推手段,能够在实验室和现场研究中助推环境跨期决策。这些结论对于理解怀孕的进化功能和基于"长计远虑"开发长远环保政策具有借鉴意义。     

行为社会政策：“助推”公共福利的实践与探索

行为社会政策是基于人的行为特点而设计, 使用“助推”作为政策工具来执行的社会政策。它是行为科学在社会政策范畴的应用, 其原理在于洞察人们做选择时的行为偏好, 以隐性且非明显经济刺激的方式谋求公众利益。从政策制定者的角度出发, 基于自愿性和信息性的统一, 可将行为社会政策中的助推策略分为增加策略、减少策略和转换策略; 三大策略被用于再住院率、儿童健康、社区养老、入学资助、就业问题、器官移植和精准扶贫等民生领域, 帮助人们做出更好的选择, 提高公共福利。作为一种新兴的方法和技术, 助推并非万能, 需要政策制定者对症下药、精确识别、权衡使用, 规避“行为官僚”, 才能真正实现国家治理现代化和社会利益最大化。     

健康行动过程取向模型的发展与前景

健康行动过程取向(HAPA)模型的提出源于健康行为社会认知模型的发展和Bandura社会认知理论的应用。在HAPA中, 健康行为变化被视为一个包括行为产生、保持和恢复在内的连续过程; 结果预期、危险感知、三种自我效能感和两类计划被用来解释意图的形成及其向行动的转化; 两个阶段的划分有助于区分三类干预对象, 建议实施菜单式干预。HAPA模型具有明显的理论优势, 也引发了有关模型实质的争论。当前和未来的研究旨在考察更多的自我调节变量、检验因果模型, 在健康行为的多个领域开展应用和干预研究。     

随机控制实验:助推脱贫的现场干预研究

消除贫困是全世界新千年的首要目标。行为经济学家和发展经济学家正借助随机控制实验和助推技术在全球范围内帮助贫困者摆脱贫困。当前采用随机控制实验帮助贫困者克服行为偏差的助推行动主要表现在帮助贫困者改善当前物质条件和提升未来保障两个方面。随机控制实验和助推技术在国内的研究和实践也有效地帮助国内贫困农民提高了身心健康水平和经济收入。未来还需加强关于随机控制实验和助推技术的外部效度检验及两者的异同点方面的研究, 并结合脱贫的内生动力制定更加简单、高效、严谨的扶贫政策和项目。     

临床决策与卫生政策

从讨论趋利性临床决策与泛企业化卫生政策的共同作用是造成我国医改失败的根本原因入手,在对3个典型案例进行深入剖析的基础上,论证了临床决策和卫生政策相互依存、相互作用的基本关系;研究了临床决策与卫生政策的决策主体、影响因素、决策过程、信息反馈、关系调整的循环路径;认为完善决策机制是保证临床决策和卫生政策正确有效的必要条件,提出了进一步完善临床决策和卫生政策的决策机制的具体意见和建议。     

The aging decision maker: cognitive aging and the adaptive selection of decision strategies

Are older adults' decision abilities fundamentally compromised by age-related cognitive decline? Or can they adaptively select decision strategies? One study (N = 163) investigated the impact of cognitive aging on the ability to select decision strategies as a function of environment structure. Participants made decisions in either an environment that favored the use of information-intensive strategies or one favoring the use of simple, information-frugal strategies. Older adults tended to (a) look up less information and take longer to process it and (b) use simpler, less cognitively demanding strategies. In accordance with the idea that age-related cognitive decline leads to reliance on simpler strategies, measures of fluid intelligence explained age-related differences in information search and strategy selection. Nevertheless, both young and older adults seem to be equally adapted decision makers in that they adjust their information search and strategy selection as a function of environment structure, suggesting that the aging decision maker is an adaptive one.     

Taking responsibility for health in an epistemically polluted environment

Proposals for regulating or nudging healthy choices are controversial. Opponents often argue that individuals should take responsibility for their own health, rather than be paternalistically manipulated for their own good. In this paper, I argue that people can take responsibility for their own health only if they satisfy certain epistemic conditions, but we live in an epistemic environment in which these conditions are not satisfied. Satisfying the epistemic conditions for taking responsibility, I argue, requires regulation of this environment. I describe some proposals for such regulation and show that we cannot reject all regulation in the name of individual responsibility. We must either regulate individuals’ healthy choices or regulate the epistemic environment.     

A Comparison of Single-Step and Multiple-Step Transition Analyses of Multiattribute Decision Strategies

Evaluating information is a fundamental component of multiattribute decision making that can be guided by one of many cognitive strategies. Considerable research has examined the factors that influence strategy selection; however, the identification of strategies remains problematic. The search sequence or transitions that a decision maker uses when searching a matrix of decision information can provide important clues to the strategy guiding the processing of decision information. The most common form of strategy analysis is to examine each transition from one piece of information to the next to establish whether these transitions are primarily alternative or attribute based. However, the resulting single-step transition indices often restrict strategy identification to a quantitative measure of compensatoriness and were found to provide conflicting results for the same search data. The current paper proposes a multiple-step transition analysis that records more complex, longer transitions to provide a multivariate profile of the strategy. Empirical support for the advantages of a multiple-step transition analysis over single-step transition indices is also provided.     

Does Time Constraint on Users Negate the Efficacy of Decision Support Systems?

Time constraint can impair decision performance: time-constrained decision makers process information faster, process less information, and use less rigorous decision strategies. On the other hand, properly designed decision support systems (DSSs) can induce decision makers to process more information and use more rigorous decision strategies, which can result in enhanced performance. In this study, we investigate, drawing on bounded rationality and cost-benefit theories of DSS use, whether these salutary effects of DSSs still hold in time-constrained environments. Our experimental results replicate past research regarding the effects of time constraint and DSSs taken separately and also show that the positive effects of DSSs are maintained when decision makers are under time constraint. That is, consistent with hypotheses, time-constrained participants processed information in a more compensatorylike manner when aided by DSSs than when not. Some of the results suggest that the negative effects of time constraint can be mitigated or even eliminated by the use of DSSs, but some participants did not take full advantage of the DSS to combat time constraint.     

The ultimate sampling dilemma in experience-based decision making

Computer simulations and 2 experiments demonstrate the ultimate sampling dilemma, which constitutes a serious obstacle to inductive inferences in a probabilistic world. Participants were asked to take the role of a manager who is to make purchasing decisions based on positive versus negative feedback about 3 providers in 2 different product domains. When information sampling (from a computerized database) was over, they had to make inferences about actual differences in the database from which the sample was drawn (e.g., about the actual superiority of different providers, or about the most likely origins of negatively valenced products). The ultimate sampling dilemma consists in a forced choice between 2 search strategies that both have their advantages and their drawbacks: natural sampling and deliberate sampling of information relevant to the inference task. Both strategies leave the sample unbiased for specific inferences but create errors or biases for other inferences.