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Table 1 The Candidate Models to Predict no-show rate across healthcare centers

From: Nudging New York: adaptive models and the limits of behavioral interventions to reduce no-shows and health inequalities

ModelDescription
0Predicting no-show status from a central intercept term, will always predict the most common outcome.
1Allows the intercept to vary by facility and borough.
2Allows the intercept to vary by facility and borough. Lead Time and Empaneled Provider have the same linear effect across all facilities and boroughs.
3Allows the intercept to vary by facility and borough. Lead Time and Empaneled Provider can have different linear effect across all facilities and boroughs.
4Allows the intercept to vary by facility and borough. Lead Time and Empaneled Provider have the same non-linear effects across all facilities and boroughs.
5Allows the intercept to vary by facility and borough. Lead Time and Empaneled Provider can interact, so that the non-linear effect of lead time depends on whether respondents are visiting their empaneled provider or not.
6Allows the intercept to vary by facility and borough. Empaneled Provider has the same effect everywhere, but lead time can have different non-linear effects in different facilities, but are shrunk towards a central, non-linear function.
7Allows the intercept to vary by facility and borough. Empaneled Provider has the same effect everywhere, but lead time can have different non-linear effects in different facilities, without any constraints on variations between facilities.