How Much Information Is Too Much? An Experimental Examination of How Information Disclosures May Unintentionally Encourage the Withholding of Health Information.

Introduction: Information disclosures are used in medicine to provide patients with relevant information. This research examines whether patients are less likely to discuss medical conditions with their physicians after seeing an insurance information disclosure.

Methods: Three experimental studies with nonprobability online samples (n = 875 US adult participants) examined the impact of information disclosures on patients' likelihood of disclosing symptoms to providers, using new symptoms and preexisting chronic conditions.

The Impact of Cultural Health Capital on Market Choice Along the Texas-Mexico Border.

Transnational cultural health capital (CHC) includes individual resources such as skills and behaviors patients use to manage healthcare exclusion and negotiate care. This study examines the impact of CHC on decisions by Hispanic people who live in El Paso, Texas, to utilize one or more markets for healthcare. We expand on the current literature and present novel findings by quantifying several aspects of CHC that may contribute to cross-border health-seeking behaviors in this vulnerable group which tends to suffer various health disparities and limited access to health insurance.

Are hospital ratings systems transparent? An examination of Consumer Reports and the Leapfrog Hospital Safety Grade.

The health care industry is complex, dynamic, and large. In such uncertain environments where a great deal of revenue is at stake, competition and comparative claims flourish. One such manifestation is hospital ratings systems.

Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux.

In this article, we attempt to clarify our statements regarding the effects of mean centering. In a multiple regression with predictors A, B, and A × B (where A × B serves as an interaction term), mean centering A and B prior to computing the product term can clarify the regression coefficients (which is good) and the overall model fit R will remain undisturbed (which is also good).

Mean centering helps alleviate "micro" but not "macro" multicollinearity.

There seems to be confusion among researchers regarding whether it is good practice to center variables at their means prior to calculating a product term to estimate an interaction in a multiple regression model. Many researchers use mean centered variables because they believe it's the thing to do or because reviewers ask them to, without quite understanding why. Adding to the confusion is the fact that there is also a perspective in the literature that mean centering does not reduce multicollinearity.