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The promise and pitfalls of cross-partisan conversations for reducing affective polarization: Evidence from randomized experiments.

ABSTRACT: Organizations, activists, and scholars hope that conversations between outpartisans (supporters of opposing political parties) can reduce affective polarization (dislike of outpartisans) and bolster democratic accountability (e.g., support for democratic norms). We argue that such conversations can reduce affective polarization but that these effects are likely to be conditional on topic, being especially likely if the conversations topics avoid discussion of areas of disagreement; usually not persist long-term; and be circumscribed, not affecting attitudes toward democratic accountability. We support this argument with two unique experiments where we paired outpartisan strangers to discuss randomly assigned topics over video calls. In study 1, we found that conversations between outpartisans about their perfect day dramatically decreased affective polarization, although these impacts decayed long-term. Study 2 also included conversations focusing on disagreement (e.g., why each supports their own party), which had no effects. Both studies found little change in attitudes related to democratic accountability.

SUBMITTER: Santoro E

PROVIDER: S-EPMC9217089 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe involvement of patients in research better aligns evidence generation to the gaps that patients themselves face when making decisions about health care. However, obtaining patients' perspectives is challenging. Amazon's Mechanical Turk (MTurk) has gained popularity over the past decade as a crowdsourcing platform to reach large numbers of individuals to perform tasks for a small reward for the respondent, at small cost to the investigator. The appropriateness of such crowdsourcing methods in medical research has yet to be clarified.ObjectiveThe goals of this study were to (1) understand how those on MTurk who screen positive for back pain prioritize research topics compared with those who screen negative for back pain, and (2) determine the qualitative differences in open-ended comments between groups.MethodsWe conducted cross-sectional surveys on MTurk to assess participants' back pain and allow them to prioritize research topics. We paid respondents US $0.10 to complete the 24-point Roland Morris Disability Questionnaire (RMDQ) to categorize participants as those "with back pain" and those "without back pain," then offered both those with (RMDQ score ?7) and those without back pain (RMDQ <7) an opportunity to rank their top 5 (of 18) research topics for an additional US $0.75. We compared demographic information and research priorities between the 2 groups and performed qualitative analyses on free-text commentary that participants provided.ResultsWe conducted 2 screening waves. We first screened 2189 individuals for back pain over 33 days and invited 480 (21.93%) who screened positive to complete the prioritization, of whom 350 (72.9% of eligible) did. We later screened 664 individuals over 7 days and invited 474 (71.4%) without back pain to complete the prioritization, of whom 397 (83.7% of eligible) did. Those with back pain who prioritized were comparable with those without in terms of age, education, marital status, and employment. The group with back pain had a higher proportion of women (234, 67.2% vs 229, 57.8%, P=.02). The groups' rank lists of research priorities were highly correlated: Spearman correlation coefficient was .88 when considering topics ranked in the top 5. The 2 groups agreed on 4 of the top 5 and 9 of the top 10 research priorities.ConclusionsCrowdsourcing platforms such as MTurk support efforts to efficiently reach large groups of individuals to obtain input on research activities. In the context of back pain, a prevalent and easily understood condition, the rank list of those with back pain was highly correlated with that of those without back pain. However, subtle differences in the content and quality of free-text comments suggest supplemental efforts may be needed to augment the reach of crowdsourcing in obtaining perspectives from patients, especially from specific populations.

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The promise and pitfalls of cross-partisan conversations for reducing affective polarization: Evidence from randomized experiments.

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