понедельник, 17 марта 2008 г.

compatibility

Comparability of contraceptive prevalence estimates for women from the 2002 Behavioral Risk Factor Surveillance System
http://www.publichealthreports.org/archives/issuecontents.cfm?Volume=123&Issue=2
Public Health Reports. 2008 Mar-Apr;123(2):147-154.
Santelli J | Lindberg LD | Finer LB | Rickert VI | Bensyl D | Posner S | Makleff S | Kost K | Singh S

This article assesses the comparability of contraceptive use estimates for adult women obtained from the 2002 Behavioral Risk Factor Surveillance System (BRFSS), using the 2002 National Survey of Family Growth (NSFG) as a benchmark. The 2002 BRFSS uses data collection methods that are considerably different from the NSFG. We compared demographic differences and national estimates of current contraceptive methods being used and reasons for nonuse. Variables were recoded in the BRFSS and NSFG systems to make the two samples comparable. Women in the NSFG and BRFSS were similar in age and race/ethnicity. Compared with the NSFG, the BRFSS sample was more educated and of higher income, less likely to be cohabiting, and more likely to be married. After adjusting for differences in the coding of hysterectomy, many BRFSS estimates for current contraceptive use were statistically similar to those
from the NSFG. Small but statistically significant differences were found for vasectomy (7.7% and 6.3%), the pill (21.9% and 19.6%), rhythm (1.5% and 1.0%), the diaphragm (0.5% and 0.2%), and withdrawal (0.3% and 2.7%) for the BRFSS and NSFG, respectively. Major reasons for nonuse were similar: seeking pregnancy and currently pregnant. The percentage of women who were not currently sexually active was higher in the BRFSS (16.0%) compared with the NSFG (12.5%). The BRFSS is a useful source of population-based data on contraceptive use for reproductive health program planning; however, planners should be cognizant that lower-income women are not fully represented in telephone surveys.

четверг, 6 марта 2008 г.

behaviorchange

A challenge to behavior change scholars and practitioners: Change your way of thinking

Author: Arvind Singhal, Ph.D., Samuel Shirley and Edna Holt Marston Endowed Professor, University of Texas El Paso

asinghal@utep.edu

The purpose of this editorial is to provoke scholars and practitioners engaged in social change, including those involved in health promotion and education, to think differently about how (behavior) change happens.

The challenge: Why are we so wedded to theorize complex health problems and behavior change goals in linear, individualistic, cognitive-processing frameworks? Are there other theories that will work?

Why do behavior change models, in spite of evidence to the contrary, view individuals as the locus of change? The dominant change frameworks (stages of change, health belief models, and the like), with some variation, essentially advocate for plugging KAP (knowledge, attitude, and practice) gaps, targeting the existing deficiencies at the individual level, moving them linearly toward desired behaviors. While they represent one way of conceptualizing behavior change and are thus of some theoretic value, such models also are problematic in that they subscribe implicitly to questionable assumptions: For instance, most individuals are capable of controlling their context, operate in a level playing field, make decisions of their free will, and mainly through a rational cognitive processing framework.

This problematic prevailing mind-set of behavior change stated as -- "if we do this to individuals, they will behave in this way" -- is a result of the overwhelming dominance of cause-effect Newtonian thinking that spilled over to social science and was reified over decades without much questioning. Newtons laws operated so well in creating predictable machines that we (mistakenly) began to believe that social and organizational systems could be built with precision using blueprints and interchangeable parts, and manipulated like machines with outputs that could be predicted, controlled, and measured. When social systems did not behave in such predictable ways, the blueprint was re-engineered, the parts interchanged, the supervision enhanced. The incremental gains that were made, reinforced the existing paradigm of prediction and control, without calling for any alternative ways of
framing the pathways to behavior change.

This editorial challenges us to seek out frameworks that question (and debunks) the machine view of a social system, recognizing that living beings cannot be controlled, manipulated, predicted, and/or replaced at free will. They cannot be hierarchically arranged as machine parts and work like clockwork, devoid of feelings, aspirations, and motivations. Lets find a framework that can simultaneously explain the certainty and uncertainty associated with outcomes, and how those outcomes could be achieved. Lets conceive a framework where outcomes can be thought about as being dynamic and emergent, and where serendipity, self-organizing, and surprise is valued, and not dismissed as anomaly. This workable framework can account for both linearity as well as non-linearity; that is, why small inputs in a social system can yield surprisingly big outcomes and why often big, expensive
interventions yield small, dismal outcomes. We need to account for the simultaneous order and disorder in a system, as well as the co-existence of paradoxes and contradictions.

One framework that views the world in most of its complexity is commonly referred to as complexity science, a discipline that provides insights into how social systems self-organize, evolve, and adapt as a result of interactions between its elements. Complexity science debunks highly-planned, linear, individual-centered cause-effect approaches to behavior change, paying attention to the quality of networked interactions, mutual causality, non-linearity, outliers, and contradictions. Nobel laureates and MacArthur "geniuses" such as Murray Gell-Mann in physics, Ilya Prigogine in chemistry, and the late Herbert Simon in psychology have been writing about it for decades; however, the the dominant model of social change, and accompanying bio-medical discourses have continued to silence such alternative ways to frame human behavior changes.

Let me illustrate with a small example from one of my research projects that speaks to the importance of looking at people not just as individuals but as connected, interdependent beings. My example points to social change not as a cause-effect linear process, but rather one characterized by small changes in system inputs that can produce big ripple effects. In 2002-2003 the Indian state of Bihar was the site of a radio soap opera broadcast called Taru, designed to promote gender equality (Singhal, Sharma, Papa, & Witte, 2004). In one episode, the feminist protagonist, Taru, arranges with a village family to celebrate the birthday of one of the little girls. In reality, girls birthdays are traditionally not celebrated in rural Bihar; whereas a boys birthday calls for ritualistic ceremony. However, after this particular episode was broadcast, several villages in Bihar reported the
celebration of girls birthdays (Singhal, Rao, & Pant, 2006). Also, once a girls birthday was celebrated, many other girls in the same village (who attended the birthday party) demanded that their birthdays be celebrated as well. When guests from neighboring villages attended these celebrations, this practice spilled over to other locations. One may ask: Is this just a new fad, complete with cakes, balloons, and sweets? Or is it more? If girls in todays rural Bihar demand that their birthdays be celebrated on par with boys, are they not likely to demand someday that they also ride the bicycle, or go to school, as their brothers do?

Where will the waves of change, riding on networked individuals and communities, stop?

References:

Lacayo Criquillion, V. (2006). Approaching Social Change as a Complex Problem in a World that Treats it as a Complicated One: The Case of Puntos de Encuentro, Nicaragua. MA Thesis. Athens, OH: Communication and Development Studies Program.

Papa, M.J., Singhal, A., & Papa, W.H. (2006). Organizing for Social Change. Newbury Park, CA: Sage.

Singhal, A. (2008). Where Social Change Scholarship and Practice Went Wrong? Might Complexity Science Provide a Way Out of this Mess? Communication for Development and Social Change, Volume 2, pp. in press.

Singhal, A., Rao, N., & Pant, S. (2006). Entertainment-Education and Possibilities for Second-Order Social Change. Journal of Creative Communications 1(3): 267-283.

Singhal, A., Sharma, D., Papa, M. J., & Witte, K. (2004). Air Cover and Ground Mobilization: Integrating Entertainment-education Broadcasts with Community Listening and Service Delivery in India. In A. Singhal, M. Cody, E. M. Rogers, & M. Sabido (Eds.), Entertainment-education and Social Change: History, Research, and Practice (pp. 351-374). Mahwah, NJ: Lawrence Erlbaum.

Stacey, R. D. (1996). Complexity and Creativity in Organizations. San Francisco, CA: Berrett-Koehler Publishers.

Wheatley, M. J. (1999). Leadership and the New Science: Discovering Order in a Chaotic World. San Francisco, CA: Berrett-Koehler Publishers.

Zimmerman, B., Lindberg, C., & Plsek, P. (1998). Edgeware: Lessons From Complexity Science for Health Care Leaders. Dallas, TX: VHA Inc.E-mail this: http://prds.infoforhealth.org/email/senditem.php?ezine_id=1&lang_id=en&article_id=18181&edition_id=1333