Upstream Lab, Li Ka Shing Knowledge Institute, MAP Centre for Urban Health Solutions, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
Department of Family Medicine, Dalhousie University, Halifax, Canada.
BMC Prim Care. 2023 Nov 25;24(1):247. doi: 10.1186/s12875-023-02173-8.
Healthcare organizations are increasingly exploring ways to address the social determinants of health. Accurate data on social determinants is essential to identify opportunities for action to improve health outcomes, to identify patterns of inequity, and to help evaluate the impact of interventions. The objective of this study was to refine a standardized tool for the collection of social determinants data through cognitive testing.
An initial set of questions on social determinants for use in healthcare settings was developed by a collaboration of hospitals and a local public health organization in Toronto, Canada during 2011-2012. Subsequent research on how patients interpreted the questions, and how they performed in primary care and other settings led to revisions. We administered these questions and conducted in-depth cognitive interviews with all the participants, who were from Saskatchewan, Manitoba, Ontario, and Newfoundland and Labrador. Cognitive interviewing was used, with participants invited to verbalize thoughts and feelings as they read the questions. Interview notes were grouped thematically, and high frequency themes were addressed.
Three hundred and seventy-five individuals responded to the study advertisements and 195 ultimately participated in the study. Although all interviews were conducted in English, participants were diverse. For many, the value of this information being collected in typical healthcare settings was unclear, and hence, we included descriptors for each question. In general, the questions were understood, but participants highlighted a number of ways the questions could be changed to be even clearer and more inclusive. For example, more response options were added to the question of sexual orientation and the "making ends meet" question was completely reworded in light of challenges to understand the informal phrasing cited by English as a Second Language (ESL) users of the tool.
In this work we have refined an initial set of 16 sociodemographic and social needs questions into a simple yet comprehensive 18-question tool. The changes were largely related to wording, rather than content. These questions require validation against accepted, standardized tools. Further work is required to enable community data governance, and to ensure implementation of the tool as well as the use of its data is successful in a range of organizations.
医疗机构越来越多地探索解决健康的社会决定因素的方法。准确的社会决定因素数据对于确定改善健康结果的行动机会、识别不公平模式以及帮助评估干预措施的影响至关重要。本研究的目的是通过认知测试完善用于收集社会决定因素数据的标准化工具。
加拿大安大略省多伦多市的一家医院和一家当地公共卫生组织在 2011 年至 2012 年期间合作开发了一套用于医疗保健环境的社会决定因素初始问题集。随后的研究探讨了患者如何解释这些问题以及他们在初级保健和其他环境中的表现，从而进行了修订。我们使用这些问题并对来自萨斯喀彻温省、马尼托巴省、安大略省和纽芬兰和拉布拉多省的所有参与者进行了深入的认知访谈。使用认知访谈，邀请参与者在阅读问题时表达想法和感受。访谈记录按主题分组，并解决了高频主题。
有 375 人对研究广告做出回应，最终有 195 人参与了研究。尽管所有访谈均以英语进行，但参与者的背景多样。对于许多人来说，在典型的医疗保健环境中收集这些信息的价值尚不清楚，因此我们为每个问题添加了描述符。总的来说，这些问题是可以理解的，但参与者强调了可以进行一些更改以使问题更加清晰和更具包容性的方式。例如，为性取向问题添加了更多的回答选项，并根据英语作为第二语言（ESL）工具使用者对理解非正式措辞的挑战，完全改写了“收支平衡”问题。
在这项工作中，我们将最初的 16 个社会人口统计学和社会需求问题集精炼成一个简单但全面的 18 个问题工具。这些更改主要与措辞有关，而不是内容。这些问题需要与公认的标准化工具进行验证。还需要进一步的工作来实现社区数据治理，并确保工具的实施及其数据的使用在各种组织中取得成功。
