Evaluating self-help support groups for medical students

Goetzel, R. Z., Croen, L. G., Shelov, S., Boufford, J. I., & Levin, G.

Publication year

1984

Journal title

Academic Medicine

Volume

59

Issue

4

Page(s)

331-340
Abstract
Self-help support groups for medical students represent one strategy for dealing with the emotional stresses of medical training and the diminished human sensitivity of students that often accompanies that experience. Support groups at the Albert Einstein College of Medicine were evaluated by 26 students who completed a nine-part questionnaire. The respondents indicated that they were primarily drawn to these groups because of a desire for social affiliation and an opportunity to express their feelings in a “safe” environment. Members shared in the leadership responsibilities of the group and dealt with external personal problems of the students rather than with the internal group dynamics. The gains derived from participation in these groups included opportunities for nonprofessional contact with faculty members, getting help and support from fellow students, and participation in stimulating discussions about the medical field. Students rated the groups as “meaningful” and expressed a desire for more frequent meetings.

Andrew Pattullo Lecture. Health future: the managerial agenda.

Boufford, J. I.

Publication year

1999

Journal title

The Journal of health administration education

Volume

17

Issue

4

Page(s)

271-295

The Lancet Commission on pollution and health

Landrigan, P. J., Fuller, R., Acosta, N. J., Adeyi, O., Arnold, R., Basu, N., Baldé, A. B., Bertollini, R., Bose-O’Reilly, S., Boufford, J. I., Breysse, P. N., Chiles, T., Mahidol, C., Coll-Seck, A. M., Cropper, M. L., Fobil, J., Fuster, V., Greenstone, M., Haines, A., Hanrahan, D., Hunter, D., Khare, M., Krupnick, A., Lanphear, B., Lohani, B., Martin, K., Mathiasen, K. V., McTeer, M. A., Murray, C. J., Ndahimananjara, J. D., Perera, F., Potočnik, J., Preker, A. S., Ramesh, J., Rockström, J., Salinas, C., Samson, L. D., Sandilya, K., Sly, P. D., Smith, K. R., Steiner, A., Stewart, R. B., Suk, W. A., Van Schayck, O. C., Yadama, G. N., Yumkella, K., & Zhong, M.

Publication year

2017

Journal title

The Lancet

The challenge of attribution: Responsibility for population health in the context of accountable care

Gourevitch, M. N., Cannell, T., Boufford, J. I., & Summers, C.

Publication year

2012

Journal title

American Journal of Public Health

Volume

102
Abstract
One of the 3 goals for accountable care organizations is to improve population health. This will require that accountable care organizations bridge the schism between clinical care and public health. But do health care delivery organizations and public health agencies share a concept of " population"? We think not: whereas delivery systems define populations in terms of persons receiving care, public health agencies typically measure health on the basis of geography. This creates an attribution problem, particularly in large urban centers, where multiple health care providers often serve any given neighborhood. We suggest potential innovations that could allow urban accountable care organizations to accept accountability, and rewards, for measurably improving population health.

The challenge of attribution: Responsibility for population health in the context of accountable care

Gourevitch, M. N., Cannell, T., Boufford, J. I., & Summers, C.

Publication year

2012

Journal title

American Journal of Preventive Medicine

Volume

42

Issue

6

A study on the relationship between adaptive ability and home environment in middle school

Li, G., & Xu, V. S.

Publication year

1999

Journal title

In Learning and Research

Page(s)

45

The belief and modeling of aging

Cui, L. J., Xu, V. S., & Wang, X. J.

Publication year

2000

Journal title

Chinese Journal of Gerontology

Volume

20

Page(s)

3

Latent curve models: A structural equation perspective

Blozis, S. A., Cho, Y., & Xu, V. S.

Publication year

2010

Journal title

Sociological Methods and Research

Volume

39

Page(s)

297

Sensitivity Analysis of Multiple Informant Models When Data Are Not Missing at Random

Blozis, S. A., Ge, X., Xu, S., Natsuaki, M. N., Shaw, D. S., Neiderhiser, J. M., Scaramella, L. V., Leve, L. D., & Reiss, D.

Publication year

2013

Journal title

Structural Equation Modeling

Volume

20

Issue

2

Page(s)

283-298
Abstract
Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes data. Statistical inference is based on the assumption that data are missing completely at random or missing at random. Importantly, whether or not data are missing is assumed to be independent of the missing data. A saturated correlates model that incorporates correlates of the missingness or the missing data into an analysis and multiple imputation that might also use such correlates offer advantages over the standard implementation of SEM when data are not missing at random because these approaches could result in a data analysis problem for which the missingness is ignorable. This article considers these approaches in an analysis of family data to assess the sensitivity of parameter estimates and statistical inferences to assumptions about missing data, a strategy that could be easily implemented using SEM software.

On Fitting a Multivariate Two-Part Latent Growth Model

Xu, S., Blozis, S. A., & Vandewater, E. A.

Publication year

2014

Journal title

Structural Equation Modeling

Volume

21

Issue

1

Page(s)

131-148
Abstract
A 2-part latent growth model can be used to analyze semicontinuous data to simultaneously study change in the probability that an individual engages in a behavior, and if engaged, change in the behavior. This article uses a Monte Carlo (MC) integration algorithm to study the interrelationships between the growth factors of 2 variables measured longitudinally where each variable can follow a 2-part latent growth model. A SAS macro implementing Mplus is developed to estimate the model to take into account the sampling uncertainty of this simulation-based computational approach. A sample of time-use data is used to show how maximum likelihood estimates can be obtained using a rectangular numerical integration method and an MC integration method.
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