NIHR Policy Research Unit in Behavioural Science-Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK.
Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, UK.
Sci Rep. 2024 Feb 9;14(1):3322. doi: 10.1038/s41598-024-53383-8.
During the COVID-19 pandemic, lateral flow tests (LFTs) were used to regulate access to work, education, social activities, and travel. However, falsification of home LFT results was a concern. Falsification of test results during an ongoing pandemic is a sensitive issue. Consequently, respondents may not answer truthfully to questions about LFT falsification behaviours (FBs) when asked directly. Indirect questioning techniques such as the Extended Crosswise model (ECWM) can provide more reliable prevalence estimates of sensitive behaviors than direct questioning. Here we report the prevalence of LFT FBs in a representative sample in England (n = 1577) using direct questioning (DQ) and the ECWM. We examine the role of demographic and psychological variables as predictors of LFT FBs. We show that the prevalence estimates of the FBs in the DQ condition were significantly lower than the ECWM estimates, e.g., reporting a negative result without conducting a test: 5.7% DQ vs 18.4% ECWM. Moral norms, subjective norms, anticipated regret, perception of risk to self, and trust in government predicted some of the FBs. Indirect questioning techniques can help provide more realistic and higher quality data about compliance with behavioural regulations to government and public health agencies.
在 COVID-19 大流行期间,侧向流动检测(LFT)被用于规范工作、教育、社交活动和旅行的准入。然而,家庭 LFT 结果的伪造是一个令人担忧的问题。在大流行期间伪造检测结果是一个敏感问题。因此,当被直接问及 LFT 伪造行为(FB)时,受访者可能不会如实回答。扩展横向模型(ECWM)等间接询问技术比直接询问能更可靠地估计敏感行为的流行率。在这里,我们使用直接询问(DQ)和 ECWM 报告了在英格兰有代表性的样本中 LFT FB 的流行率(n=1577)。我们研究了人口统计学和心理变量作为 LFT FB 预测因子的作用。我们发现,DQ 条件下 FB 的流行率估计明显低于 ECWM 估计,例如,报告未进行测试的阴性结果:DQ 为 5.7%,而 ECWM 为 18.4%。道德规范、主观规范、预期后悔、对自身风险的感知以及对政府的信任预测了一些 FB。间接询问技术可以帮助政府和公共卫生机构提供更现实和更高质量的数据,了解对行为规范的遵守情况。