Assessment of patient satisfaction: development and refinement of a service evaluation questionnaire

A series of seven studies was conducted by the authors and their colleagues to produce an efficient measure of service satisfaction that can easily be related to symptom level, demographic characteristics, and type and extent of service utilization. The resulting measure, the Service Evaluation Questionnaire (SEQ) is a brief, global index that has excellent internal consistency and solid psychometric properties. Data from an extensive SEQ field study can be used as a comparison base for future applications of the two SEQ component scales, the CSQ-8 and the SCL-10. A new hypothesis has emerged from this series of studies that will guide future research: Service recipients may find if difficult to formally express dissatisfaction in the face of significant caring--however ineffectual--when the technical capacity to offer definitive treatment is not yet fully developed and when criteria for evaluating the efficacy of treatment are not yet crystal clear.     

Patient satisfaction with mental health services: a meta-analysis to establish norms

Patients typically express high rates of satisfaction with their mental health care. This finding and the lack of well controlled studies on patient satisfaction in the literature underscore the need for meaningful guidelines for clinicians and program evaluators in interpreting patient satisfaction data. To address this problem a meta-analysis was undertaken to establish norms on patient satisfaction for various types of mental health programs. Programs were categorized according to three dimensions: inpatient vs. outpatient vs. residential care; chronic vs. non-chronic; and conventional vs. innovative. Meta-analysis procedures were modified to accommodate the single-group study designs that dominate the literature. The analysis revealed that chronic patients express less satisfaction with their treatment compared to non-chronic patients. Innovative programs are viewed more positively than conventional ones. No differences were found in rates of patient satisfaction between inpatient and outpatient programs. Acceptably reliable norms and confidence intervals of patient satisfaction were established for conventional inpatient programs serving either chronic or non-chronic patients; conventional outpatient programs for non-chronic patients; and for all programs combined according to chronic vs. non-chronic, inpatient vs. outpatient, and conventional vs. innovative. However, data were insufficient to compute norms for other program types. The norms thus established can be used for comparative purposes by program evaluators. A cumulative, national data base on patient satisfaction is recommended to further refine these norms.     

Analysis of social pathologies in a policy context: A paradigm for action

Prevention continues to gain importance among the strategies open for governmental development of social policy. This paper offers two exploratory conceptual discussions related to prevention in social policy. The first is a representation of the policy making environment with special reference to the informational requirements for the development of policy. The second is a discussion of social pathologies, broadly defined as patterns of behavior with important negative repercussions on individuals whom we refer to as victims, and of possible strategies toward the development of (at least partial) preventive measures. The two discussions are interrelated as the shape of the second is dictated by the desiderata outlined in the first. The paper is divided into five parts:

1. Introduction: the conundrum of prevention.
2. The Development of a paradigm of the policy context.
3. Dependent variables: social pathologies.
4. Independent variables: a focus on structural determinants.
5. Conclusion: information requirements for preventive action.

     

Demographic crisis: The impact of the Bangladesh civil war (1971) on births and deaths in a rural area of Bangladesh

Summary In Matlab Bazaar Thana the Cholera Research Laboratory has registered the births, deaths and migrations in a population of approximately 125,000 since 1966. Although this rural area was not the scene of any significant armed encounters, striking changes in birth and death rates were registered during and after the conflict. Birth rates did not change during the relatively brief period of the civil war, but a small decline was registered for one year after the war. Fertility rates which had been declining slightly and irregularly in the pre-war baseline period may have increased slightly during the war and fell substantially in all age groups in the year following the war. The crude death rate, which rose by 37 per cent during the war, was a very sensitive reflection of the administrative and economic problems. Overall infant mortality rose by only 15 per cent over pre-war levels because all of the increase was observed in the post-neo-natal component, which traditionally accounts for less than one-third of the total infant mortality in Bangladesh. Children and older adults accounted for the majority of excess deaths which were largely attributed to acute diarrhoeas and other gastro-intestinal causes. The death rate at ages 1-4 rose by 43 per cent and at ages 5-9 soared to 208 per cent above pre-war baseline rates. All increases in age-specific mortality rates fell to baseline levels during the year following the war, except the 5-9-year age group, in which rates continued to be high largely because of deaths due to dysentery.     

Decision Analysis and Risk Management Decision Making: Issues and Methods

This paper provides an overview of decision analysis and its use in risk management decision making. The paper discusses the distinctive characteristics of decision analysis and compares these characteristics with those of its principal alternative—cost–benefit analysis. The paper also discusses each of the steps in a decision analysis and the strengths and limitations of the method.     

Regression coefficient analysis for correlated binomial outcomes

At present, the generalized estimating equation (GEE) and weighted least-squares (WLS) regression methods are the most widely used methods for analyzing correlated binomial data; both are easily implemented using existing software packages. We propose an alternative technique, i.e. regression coefficient analysis (RCA), for this type of data. In RCA, a regression equation is computed for each of n individuals; regression coefficients are averaged across the n equations to produce a regression equation, which predicts marginal probabilities and which can be tested to address hypotheses of different slopes between groups, slopes different from zero, different intercepts, etc. The method is computationally simple and can be performed using standard software. Simulations and examples are used to compare the power and robustness of RCA with those of the standard GEE and WLS methods. We find that RCA is comparable with the GEE method under the conditions tested, and suggest that RCA, within specified limitations, is a viable alternative to the GEE and WLS methods in the analysis of correlated binomial data.     

Reconstructing the size of the African American population by age and sex, 1930–1990

We estimate the size of the African American population in five-year age groups at census dates from 1930 to 1990 using a three-part strategy. For cohorts born after 1935, we follow the U.S. Census Bureau in using classical demographic analysis. To estimate the size of cohorts born before 1895, we use extinct-generation estimates. For remaining cohorts, we implement an age/period/cohort model of census counts. All approaches are applied to a data set in which the age distribution of deaths has been corrected for age misreporting. Results provide strong confirmation of the basic validity of Census Bureau estimates of census undercounts for African Americans while extending estimates to new cohorts and periods. Our estimates are less consistent with an historical series prepared by Coale and Rives (1973).