Structural equation models raw data. by K. Kanoho Hosoda (19929050)

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Intercorrelation matrix of kindness, institutional identity, stress, and well-being. by K. Kanoho Hosoda (19929050)

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Changes in blood pressure, HbA1c, blood lipids and liver function-related indicator values from 3 months to 6 months of treatment in all patients. by Jia Song (495396)

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Changes in the UACR and urinary β2-MG levels compared with baseline values over time. by Jia Song (495396)

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Changes in the eGFRs and serum potassium levels compared with baseline values in all patients over time. by Jia Song (495396)

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Comparison of the rates of decline in the UACR and urinary β2-MG levels between the two subgroups. by Jia Song (495396)

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Model effects of receiving kindness on reduced stress and well-being mediated by institutional identity. by K. Kanoho Hosoda (19929050)

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Changes in the eGFRs and serum potassium levels compared with values at the end of 1 month of treatment in all patients over time. by Jia Song (495396)

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Changes in inflammatory cytokine levels compared with baseline values in all patients over time. by Jia Song (495396)

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Model effects of giving kindness on reduced stress and well-being mediated by institutional identity. by K. Kanoho Hosoda (19929050)

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Effects of finerenone on the urinary β2-MG levels in all patients. by Jia Song (495396)

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Conceptual model being tested in the current study. by K. Kanoho Hosoda (19929050)

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Baseline characteristics of the participants. by Jia Song (495396)

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Summary of sample descriptive statistics. by K. Kanoho Hosoda (19929050)

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Preference for the EIA – conjoint results. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Marginal means – Pooled across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample attribute table. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Subgroup analysis – Political affiliation. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample scenario description. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

AMCEs – Pooled across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”