Quality of Life Predictors and Normative Data

This study identifies predictors and normative data for quality of life (QOL) in a sample of Portuguese adults from general population. A cross-sectional correlational study was undertaken with two hundred and fifty-five (N = 255) individuals from Portuguese general population (mean age 43 years, range 25–84 years; 148 females, 107 males). Participants completed the European Portuguese version of the World Health Organization Quality of Life short-form instrument and the European Portuguese version of the Center for Epidemiologic Studies Depression Scale. Demographic information was also collected. Portuguese adults reported their QOL as good. The physical, psychological and environmental domains predicted 44 % of the variance of QOL. The strongest predictor was the physical domain and the weakest was social relationships. Age, educational level, socioeconomic status and emotional status were significantly correlated with QOL and explained 25 % of the variance of QOL. The strongest predictor of QOL was emotional status followed by education and age. QOL was significantly different according to: marital status; living place (mainland or islands); type of cohabitants; occupation; health. The sample of adults from general Portuguese population reported high levels of QOL. The life domain that better explained QOL was the physical domain. Among other variables, emotional status best predicted QOL. Further variables influenced overall QOL. These findings inform our understanding on adults from Portuguese general population QOL and can be helpful for researchers and practitioners using this assessment tool to compare their results with normative data.     

Spiritual Needs and Quality of Life in Estonia

This paper reports on three field studies using the WHOQOL-100 and WHOQOL-BREF instruments that utilized three different samples (N = 1,801) to get a better understanding of how important the person’s spiritual needs are for quality of life. The most striking negative difference between the Estonian and World Health Organization samples was in the WHOQOL-100 spirituality domain. We found that the quality of life index significantly correlated with the WHOQOL-100 spirituality score. Also, spirituality was related to all quality of life domains (physical health, psychological well-being, level of independence, social relationships and environment). Regarding psychological well-being, spirituality correlated with self-esteem, positive feelings, and thinking, learning, memory, and concentration, on the other. Our findings suggest that spirituality occupies an important place in the person’s perception of their quality of life in a changing socio–economic environment as the one in Estonia.     

Health and quality of life of older people,a replication after six years

Replicating a survey of 875 people 55 years old or more undertaken in September 1999 throughout the former Northern Interior Health Region (NIHR) of British Columbia, in September 2005 a sample of 656 people completed a 22-page questionnaire. The average age of the respondents was 68, with a range running from 55 to 96 years, and 64% were women. Responses to the SF-36 questionnaire indicated that for male respondents aged 55–64, the mean score for the 8 dimensions was 76.1. This mean was a bit higher than the 74.4 mean of 1999. For male respondents aged 65 and older the mean was 69.0, which was also higher than the 68.3 mean of 1999. For females aged 55–64, the mean score for 8 dimensions was 73.1, versus 73.0 in 1999. For female respondents aged 65 and older, the mean score was 67.0, versus 65.4 in 1999. Based on these mean scores for the 8 dimensions, then, it is fair to say that the overall health status of males and females aged 55 years and older in the region in 2005 was at least as good as (i.e., the same as or better than) that in 1999. Comparing 28 average figures for the 2005 respondents on satisfaction with specific domains of life (e.g., financial security, health, sense of meaning) and three global indicators (satisfaction with life as a whole and with the overall quality of life, and happiness) with those of the 1999 respondents, we found that the scores for the 2005 sample were at least as high as those of the other sample. Thus, it seems fair to say that the perceived quality of life of older people in the former NIHR so far as it is revealed in domain and global satisfaction and happiness scores, is at least as good as the perceived quality of life of a similar sample in 1999. Although a large majority perceived increases in crime in the 2 year periods prior to both surveys, smaller percentages of the 2005 sample than of the 1999 sample thought that crime had increased over the past two years, avoided going out at night, feared for their safety, had crime-related worries, engaged in crime-related defensive behaviours and were actually the victims of any crimes. Therefore, it seems fair to say that, so far as crime-related issues are concerned for the two samples of seniors responding to our surveys, there is more evidence of improvement than of deterioration. Applying stepwise multiple regression, each of the eight dependent variables was explained on the basis of four clusters of predictors separately and then a final regression was run using only the statistically significant predictors from the four clusters. Broadly speaking, 7 SF-36 health status scales explained from 28% to 45% of the variance in the 8 dependent variables, running from satisfaction with the overall quality of life (28%) to the single item measure of general health (45%). The seven predictors in the Social Relations cluster explained from 7% of the variance in the SF-36 General Health scale scores to 57% of the variance in the Life Satisfaction scores. The four predictors in the Problems cluster explained from 10% of the variance in the SF-36 General Health scale scores to 24% of the variance in the SWLS scores. The 11 predictors in the Domain Satisfaction cluster explained from 14% of the variance in the SF-36 General Health scale scores to 64% of the variance in the SWB scores. Putting all the significant predictors together for each dependent variable, in the weakest case, 4 of 11 potential predictors explained 33% of the variance in the SF-36 General Health scale scores and in the strongest case, 9 of 15 potential predictors explained 70% of the variance in Life Satisfaction scores. Among other things, these results clearly show that respondents’ ideas about a generally healthy life are different from, but not independent of, their ideas about a happy, satisfying or contented life, or about the perceived quality of their lives or their subjective wellbeing. Finally, the 7 core discrepancy predictors of MDT plus incomes were used to explain the eight dependent variables. From 13% of the variance in the SF-36 General Health scale scores to 57% of the variance in SWLS scores was explained using those predictors. Based on an examination of the Total Effects scores for the predictors of the 8 dependent variables, the most influential predictors were Self-Wants, followed by Self-Others and then Self-Best. In other words, the most influential discrepancy predictors of respondents’ overall life assessments were those between what respondents have versus what they want, followed by what they have versus what others of the same age and sex have, and then by what they have versus the best they ever had in the past. We would like to thank the Social Sciences and Humanities Research Council of Canada for support of this research with funds granted to Alex C. Michalos through the Gold Medal Award in 2004.     

Impact of health and fitness-related behavior on quality of life

While the relationships between (a) health behavior and health status and (b) health status and perceived quality of life (QOL) have received some attention, the association between health behaviors and QOL has not been determined. The primary objective of this study was to assess the effects of health behaviors on QOL that are independent of the effects of health status. A sample of approximately 5 000 randomly selected U.S. Navy personnel was split into halves and analyses performed on each to establish the replicability of the findings. At step one of a multiple regression procedure, health status variables were forced into the equation; next, health behavior variables were entered. As expected, the block of health status variables was significantly related to QOL: self-assessed health and fitness status and lower reporting of physical symptoms accounted for 16% and 18% of the variance in QOL for the two subsamples. After controlling for health status, two behavioral measures made unique contributions to the prediction of QOL: behaviors related to avoiding unnecessary risks as a driver or pedestrain and avoiding or minimizing accidents. Wellness maintenance behaviors also were associated with QOL in one subsample. After controlling for health status, health behavior measures contributed an additional 11% and 6% of the explained variance in QOL for the two subsamples. Results indicate that health behaviors influence QOL independently of health status.     

Healthy Days,Health Satisfaction and Satisfaction with the Overall Quality of Life

In this note we report results of 6 surveysusing the United States Centers for DiseaseControl and Prevention indicators of healthstatus, taken from the Behavioral Risk FactorSurveillance System. Generally speaking, wefound that the CDC healthy days variables couldplay a useful role in survey research aimed atassessing the impact of measured health statuson people's satisfaction with their own healthand with the overall quality of their lives.More precisely, using stepwise regressions wefound: (1) The three healthy days variablesexplained from 16 to 27 percent of the variancein General Health scores. The limited activitydays variable remained as a significantpredictor in only one of the six regressionsand the not good physical health days variablewas most influential in every sample. (2) Thethree healthy days variables explained from 19to 32 percent of the variance in healthsatisfaction scores, and the not good physicalhealth days variable was again most influentialin every sample. (3) The three healthy daysvariables explained from 12 to 39 percent ofthe variance in quality-of-life satisfactionscores, with the not good mental healthvariable most influential in every sample. (4)The four CDC variables together explained from40 to 55 percent of the variance in healthsatisfaction scores, with the General Healthvariable always dominating the set ofpredictors by a fairly wide margin. (5) Thefour CDC variables together explained from 17to 28 percent of the variance inquality-of-life satisfaction scores, with theGeneral Health variable most influential inthree samples and the not good mental healthvariable most influential in the other three.(6) When the four CDC variables plus the healthsatisfaction variable were used as potentialpredictors, we were able to explain from 29 to40 percent of the variance in quality-of-lifesatisfaction scores. In every sample, theGeneral Health and limited activity daysvariables had no significant impact. (7) Usingstructural equation modeling, we found GeneralHealth does not have a direct effect onsatisfaction with the overall quality of lifebut rather only an indirect effect throughhealth satisfaction. The General Health, notgood physical health, and not good mentalhealth days variables account for 51% of thevariation in health satisfaction, and healthsatisfaction, not good physical health and notgood mental health days variables account for30% of the variation in satisfaction with theoverall quality of life.     

The Use of Common Area <Emphasis Type="Italic">k</Emphasis>-Sample Test in Evaluating Targeting Methodologies: An Application to the Case of Egypt

The quality of life (QOL) is a measure of social wellbeing and life satisfaction of individuals in an area. Measuring its spatial dynamics is of great significance as it can assist the policy makers and practitioners in improving the balance between urbanization and living environment. This study proposes an approach to spatially map and examine the relationships between QOL, land use/land cover (LULC) and population density in an urban environment. The city of Lahore, Pakistan was selected as the case study area. The QOL was evaluated through the data related to physical health, psychological, social relationships, environment (natural and built), economic condition and development, and access to facilities and services. The weights/relative importance of each QOL domain was determined through the analytic hierarchy process by processing the data collected from local field experts. Overall QOL was computed by applying the domain weights to the data; spatial mapping of QOL domains and overall QOL was conducted afterwards. The spatial dynamics of QOL were examined, and its interrelationships with LULC and population density were analyzed. The relationship between these three variables turned out to be spatially dynamic. The proposed approach assists the spatial mapping and analyses of QOL, LULC and population, and by examining the spatial dynamics of these variables, contributes to devising appropriate land management and QOL improvement strategies and policies in the metropolitan regions.     

Reliability and Validity of the World Health Organization Quality of Life: Brief Version (WHOQOL-BREF) in a Homeless Substance Dependent Veteran Population

With the high number of homeless, there is a critical need for rapid and accurate assessment of quality of life to assess program outcomes. The World Health Organization’s WHOQOL-100 has demonstrated promise in accurately assessing quality-of-life in this population. However, its length may make large scale use impractical for working with a homeless population. The World Health Organization Quality of Life—Brief version (WHOQOL-BREF), though providing a shorter instrument and being, theoretically, more manageable has not been evaluated for use with homeless individuals. This study evaluated the psychometric properties of the WHOQOL-BREF in 389 homeless veterans. Adequate internal consistency for all domains was found and validity for each domain was supported using the Personality Assessment Inventory. The WHOQOL-BREF provides a reliable, valid, and brief assessment of quality-of-life. Due to the length of the instrument and the domains covered, it could have great utility in the assessment of homeless populations with multiple problem domains.     

Determinants of Health and the Quality of Life in the Bella Coola Valley

The aim of this investigation is to obtain some baseline self-reported data on the health status and overall quality of life of all residents of the Bella Coola Valley of British Columbia aged 17 years or older, and to measure the impact of a set of designated health determinants on their health and quality of life. In the period from August to November 2001, a variety of procedures were used to ensure that all eligible residents of the Valley received a copy of our questionnaire, and 687 useable questionnaires were obtained for our working dataset. Health status was measured by SF-36 and the U.S. Centers for Disease Control healthy days items. Thirty-one items were used to measure the Provincial Health Officer’s designated determinants of health in four clusters, namely, biological, social and economic, health behaviours and health services determinants. Quality of life was measured by satisfaction levels in 13 specific domains of life (e.g., family, financial security), four global items (e.g., happiness, life satisfaction) and one global Subjective Well-Being Index. Besides obtaining baseline figures on all our measures for the Valley, we made some comparisons among our figures and those from other areas, e.g., Prince George, BC. Most of the measures indicated that the health status and quality of life of Bella Coola Valley residents were lower than those of Prince George residents. For the sample as a whole, SF-36 scores on the eight dimensions ran from 82.3 (physical functioning) to 50.0 (social functioning), with a mean of 62.7. Residents in the Valley averaged 6.5 days in the past 30 in which their health was physically not good, 5.5 days when it was mentally not good and 4.1 days when their health limited their usual daily activities. Eleven percent of respondents described their general health as “excellent” and another 27% said it was “very good”. On a 7-point scale from 1=very dissatisfied to 7=very satisfied, respondents had average life satisfaction and satisfaction with the overall quality of life scores of 5.5. For specific domains of life, the lowest mean level of satisfaction was reported for federal and provincial government officials (3.3) and the highest was reported for living partners and personal safety around home (5.8). Regarding bivariate relations, each of the eight dimensions of SF-36 was significantly correlated with a single item measure of general health, and five of the eight were significantly correlated with the number of good health days. Happiness and the Subjective Well-Being Index were positively but moderately correlated with six of the eight dimensions, and life satisfaction was positively correlated with five. Age was negatively related to general health, but positively related to life satisfaction. Not being of aboriginal descent was positively related to all of the four global health indicators and to the Subjective Well-Being Index. Education was positively related to the four global health measures but not to the three global quality of life measures. The Social Support and Good Family Indexes were positively related to all seven global measures. There was a positive correlation between six of the seven global measures and the frequency with which respondents participated in activities sponsored by voluntary organizations. Frequency of smoking was negatively associated with every global dependent variable except the Physical Health Index. Frequency of skipping meals was negatively associated and average hours of sleep per night was positively associated with all seven global measures. Turning to multivariate relationships, the four clusters of health determinants explained from 12% (SF-36 Mental Health Index) to 24% (general health) of the variance in the dependent global health variables, and from 20% (happiness) to 26% (Subjective Well-Being Index) of the variance in the dependent global quality of life variables. Adding domain satisfaction scores to the total set of predictors allowed us to explain from 20% (SF-36 Mental health Index) to 29% (general health) of the variance in the dependent global health variables, and from 39% (happiness) to 62% (life satisfaction) in the dependent global quality of life variables. By including measures of social support and good family relationships in our set of health determinants, we practically guaranteed that the latter would be relatively strongly predictive of global quality of life.     

On the Predictive Effect of Multidimensional Importance-Weighted Quality of Life Scores on Overall Subjective Well-Being

This study aimed to examine the effectiveness of importance weighting in predicting outcome variables in a hierarchical and multidimensional measurement context. A total of 146 undergraduate students (female = 76; mean age = 20.25) from two universities in Taiwan and China participated in this study. They evaluated their quality of life on 22 facets from the WHOQOL-BREF scale, which covers four domains (i.e., physical health, psychological health, social relationships, and environmental health). They were also asked to rate the importance of these 22 facets and items for three general subjective well-being indices, including overall quality of life, general health, and life satisfaction. A multiplicative formula was used to create importance-weighted scores for each facet, and four domain scores were obtained by averaging facet scores under specific domains. Results of regression analysis revealed that after applying the weighting procedure, the four domain scores did not account for more variances in the three indices for overall subjective well-being, and predictive effects of the four domain scores became less differential. Our findings suggest that importance weighting did not have its expected benefits but instead may negatively impact the predictive effects.     

Gender differences in relating life events and well-being in elderly individuals

Two hundred and sixty-eight community-residing elderly participants completed measures of physical illness, psychiatric symptomatology, life satisfaction, and recent mood, and a modified version of the Rahe (1975) Recent Life Change Questionnaire on which they indicated how much adjustment each event experienced required and whether it was appraised as expected or unexpected, desirable or undesirable, and controllable or uncontrollable. The results suggest that: (1) scores that reflect how events were appraised accounted for more variance than total frequency scores; (2) optimal predictors differed for different outcome measures; and (3) there are substantial gender differences in the pattern of relationships of predictor to outcome variables. The first two findings are consistent with those reported for younger cohorts. The third finding has not been reported previously.     

Parsimony in personality: predicting sexual prejudice

Extant research has established numerous demographic, personal-history, attitudinal, and ideological correlates of sexual prejudice, also known as homophobia. The present study investigated whether Five-Factor Model (FFM) personality domains, particularly Openness, and FFM facets, particularly Openness to Values, contribute independent and incremental variance to the prediction of sexual prejudice beyond these established correlates. Participants were 117 college students who completed a comprehensive FFM measure, measures of sexual prejudice, and a demographics, personal-history, and attitudes-and-ideologies questionnaire. Results of stepwise multiple regression analyses demonstrated that, whereas Openness domain score predicted only marginal incremental variance in sexual prejudice, Openness facet scores (particularly Openness to Values) predicted independent and substantial incremental variance beyond numerous other zero-order correlates of sexual prejudice. The importance of integrating FFM personality variables, especially facet-level variables, into conceptualizations of sexual prejudice is highlighted. Study strengths and weaknesses are discussed as are potential implications for prejudice-reduction interventions.     

Multidimensional Quality of Life: A New Measure of Quality of Life in Adults

This paper presents a new measure for assessing quality of life (QOL) –the Multidimensional Quality of Life (MQOL)– and describes its derivation, characteristics, structure and several applications. Reasons for developing the MQOL include the restricted range of assessed domains and the heavy emphasis on health in many standard assessment tools. The MQOL was derived by meaning probes into QOL in different samples. It is a 60-item self-report tool of high reliability and validity covering various themes and forming, in line with factor and cluster analyses, 17 scales that constitute five factors according to confirmatory factor analysis. It has been applied with thousands of individuals, in English, Hebrew, Russian and Arabic, and is adequate for healthy and physically or mentally sick individuals, under regular or challenging circumstances. Described studies present findings in samples of sick or healthy individuals (e.g., unemployed, members of a collapsing Kibbutz); relations between the MQOL and coping strategies in partners of sick individuals; and interrelations of overall and scale scores in new and old immigrants. Conclusions focus on the structure of the MQOL, the specificity of coping effects, and the stabilizing mechanisms of QOL.     

Impact of Acts of Discrimination on Quality of Life Among Injecting Drug Users in Delhi, India

The study examines the association between quality of life (QOL) and discrimination perpetrated against a vulnerable population like injecting drug users (IDU). Given that QOL affects self efficacy which in turn affects behavior, it is relevant to examine QOL among IDUs in the context of HIV prevention, and to study whether discriminations and human rights abuses impact QOL in this population. A cross sectional study was conducted in two research sites in Delhi, India among 343 IDUs recruited through a respondent driven sampling. A Hindi version of the WHOQOL Bref survey along with a survey questionnaire of discrimination were used to interview participants. After controlling for demographic characteristics, experiencing physical and verbal abuse (OR: 0.46, CI 0.27–0.79),arrests and imprisonment for carrying needles and/or using drugs (OR: 0.53, CI 0.31–0.90) and lacking health information (OR: 0.49, CI 0.29–0.85)was associated with lower social QOL, while being denied health care services was associated with lower psychological QOL. The more discrimination experienced, the lower was the quality of life in the social and psychological domains. Participants’ perceived well being in the four domains was related to their living conditions, discriminatory acts and to perceptions of social support. Discriminatory acts and abuses appeared to have a greater toll on their psychological well being and social relationships, thus indicating the need for human rights advocacy in order to influence law enforcement practices and to reduce stigma, while expanding social support through an extended comprehensive IDU programme.     

The impact of life events and changes in domain satisfactions on well-being

Previous research has shown that QOL measures are reasonably reliable and valid for static analysis. This article is concerned with the validity of the measures for use in panel studies/dynamic analysis. Are they sensitive enough to register changes (i.e., enhanced satisfaction and well-being) of the kind one would expect to follow favourable life events, and also changes (i.e., reduced satisfaction and well-being) due to adverse life events? Also, do changes in satisfaction with specific domains of life produce concomitant changes in the general sense of well-being? These questions are addressed by means of a small panel study (N=184) in which respondents were interviewed first in November 1978 and a second time in January 1981. The questionnaire included a more detailed life events inventory (Dohrenwendet al., 1978) and one which contained a larger number of favourable events than inventories included in previous QOL panel studies (Campbellet al., 1976; Atkinson, 1982). Unlike previous researchers, we found some statistically significant relationships between aggregate life events scores and changes in well-being. Changes in relatively affective measures of well-being were greater than in relatively cognitive measures. We also found strong, predictable relationships between changes in domain satisfactions and changes in the general sense of well-being.     

Interpreting the WHOQOL-Brèf: Preliminary Population Norms and Effect Sizes

Since publication use of the WHOQOL-Brèf has rapidly risen. However, as yet no population norms have been published as a reference point against which researchers can interpret their findings. This study provides preliminary population norms for this purpose. Randomly sampled community residents from two studies were pooled and used to examine the properties of the WHOQOL-Brèf by age group, gender and health status. The results showed that general norms for the WHOQOL-Brèf domains were 73.5 (SD=18.1) for the Physical health domain, 70.6 (14.0) for Psychological wellbeing, 71.5 (18.2) for Social relationships and 75.1 (13.0) for the Environment domain. In general scores declined slightly by age group. For females scores were stable across the lifespan with an accelerated decline after the age of 60 years. Males exhibited a more consistent and even decline across the lifespan. There were significant differences in WHOQOL-Brèf scores when reported by health status, with those in poor health obtaining scores that were up to 50% lower than those in excellent health. Effect sizes between different health status levels are reported. These preliminary norms and effect sizes may be used as reference points for interpreting WHOQOL-Brèf scores. They provide additional information to the numerous national studies already reporting on the validity of the WHOQOL-Brèf.     

The individual cost of sick leave

This paper aims to estimate the causal effect of sick leave on subsequent earnings and employment, using an administrative dataset for Norway. To obtain experiment-like variation in sick leave among otherwise similar workers, the leniency of these workers’ physicians—certifying sickness absences—is used as an instrumental variable for sick leave. A 1 percentage point increase in a worker’s sick leave rate is found to reduce his earnings by 1.2% 2?years later. Around half of the reduction in earnings can be explained by a reduction of 0.5 percentage points in the probability of being employed.     

Subjective Quality of Life Among Individuals who are Homeless: A Review of Current Knowledge

The purpose of this article was to review the current literature on subjective quality of life (SQOL) in individuals who are homeless, with a focus on differences in SQOL (a) between homeless individuals and the general population, (b) based on housing situation, and (c) associated with demographic characteristics (such as age and gender), physical and mental health, and external variables such as service program type. A literature search was conducted of the online databases PubMed and PsycInfo for relevant studies published from January 1981 to August 2011. Although this review showed that individuals who are homeless tended to have lower levels of SQOL compared to the general population or housed individuals, it was also evident that our current understanding of the relationships between SQOL and various demographic, health, and other variables is based on very limited information. More information about the relationships between various characteristics and experiences of individuals who are homeless and SQOL is clearly needed to aid researchers, service providers, and policy-makers in addressing the needs of this population and examining the effectiveness of interventions to end homelessness and improve health among homeless individuals.     

Good Health is Not the Same as a Good Life: Survey Results from Brandon,Manitoba

The aim of this investigation was to obtain some baseline self-reported data on the health status and overall quality of life of a sample of residents of the city of Brandon, Manitoba aged 18 years or older, and to measure the impact of a set of designated health determinants, comparison standards and satisfaction with diverse domains of life on their health and quality of life. In May and June 2010, 2,500 households from the city of Brandon, Manitoba were randomly selected to receive a mailed out questionnaire and 518 useable, completed questionnaires were returned. Baseline health status data were obtained using the 8 SF-36 dimensions of health and 13 items from the United States Centers for Disease Control and Prevention, Behavioral Risk Factor Surveillance System. Determinants of health and overall quality of life included measures of socializing activities, a Good Neighbourhood Index, Social Support Index, Community Health Index, a measure of free-time exercise levels, health-related behaviours, use of drugs, health care issues, a set of domain-specific quality of life items, a set of measures concerning criminal victimization, worries and behaviours concerning victimization and the basic postulates of Multiple Discrepancies Theory. Overall life assessment, dependent variables included Average Health, happiness, a single item measure of satisfaction with life as a whole, a single item measure of satisfaction with the overall quality of life, the Satisfaction With Life Scale, Contentment with Life Assessment Scale and a Subjective Wellbeing Index. Using multiple regression, we explained as much as 75% of the variance in Subjective Wellbeing scores and as little as 45% in happiness scores. Four clusters of health determinants explained from 20% (Happiness) to 44% (Average Health) of the variance in the dependent variables. Adding comparison standards and domain satisfaction scores to the set of health determinants increased our total explanatory power by only 2% points for Average Health (from 44 to 46%), but more than doubled our explanatory power for Happiness (from 20 to 45%) and for satisfaction with the overall quality of life (from 31 to 67%). As well, our explanatory power for the single item of Life Satisfaction increased from 34 to 66%, for the Satisfaction With Life Scale from 39 to 74%, for the Contentment With Life Assessment Scale from 36 to 60%, and for Subjective Wellbeing from 42 to 75%. This provided very clear evidence that self-perceived good health is not equivalent to perceived quality of life, confirming evidence reported in our earlier studies. The three most important take-home messages from this investigation are (1) in assessing the relative influence of any alleged determinants of health and the quality of life, different sets of alleged determinants will appear to be more or less influential for different dependent variables. Therefore, (2) researchers should use diverse sets of determinants and dependent variables and (3) it is a big mistake to use measures of health status as if they were measures of the perceived quality of life.     

Functional consequences of work-related spinal musculoskeletal symptoms in a cohort of Australian midwives

BackgroundEarly evidence suggests spinal musculoskeletal symptoms are as prevalent in Australian midwives as in samples of nurses. Functional consequences of these symptoms include sick leave and functional incapacity, which are costly at both individual and workplace levels. To date there have been no studies of these consequences in midwives.QuestionWhat risk factors are associated with sick leave and functional incapacity among midwives with spinal musculoskeletal symptoms?MethodsWe undertook a cross-sectional study of qualified Australian midwives who completed the baseline survey of the Nurses and Midwives e-Cohort Study. A comprehensive set of independent variables were examined for bivariate associations with the main outcomes of sick leave and functional incapacity due to work-related musculoskeletal symptoms in the neck, upper or lower back. Associations that achieved a p value < .1 were entered into multiple logistic regression models.Findings729 midwives with a mean age of 46 years were eligible for inclusion. Functional incapacity was more than twice as common as sick leave. Severity of worst pain was the explanatory variable most strongly associated with each main outcome and the only one significant for both. Psychological job demands showed a significant association with sick leave, while several individual factors were associated with both outcomes. Only the association of poorer general health with functional incapacity remained significant in all three spinal regions.ConclusionOur sample reported considerable work-related musculoskeletal pain and functional incapacity. Factors associated with sick leave and functional incapacity in midwives should be confirmed by longitudinal studies with the aim of developing tertiary prevention strategies.     

Values,Performance and Subjective Well-Being in the Sustainability Movement: An Elaboration of Multiple Discrepancies Theory

Building on multiple discrepancies theory (MDT) as developed by Michalos (1985, 1991), the present study seeks to extend the application of MDT by operationalizing multiple discrepancies or “gaps” between values and performance for a representative group from the sustainability movement (back-to-the-landers), and then comparing the ability of the operationalized discrepancies, in competition with a series of demographic and process variables, to explain variance in a set of subjective well-being (SWB) measures. In the analysis of variance (ANOVA) and zero-order correlation equations, the value-performance discrepancies demonstrate consistent statistically significant relationships with the SWB measures. In the multiple regression models, however, the gap variables are not as prominent as other process variables, although they do add variance to SWB, validating the general utility of MDT. It appears, though, that there are other dimensions, many of them specific to the back-to-the-land way of life, that can compensate for the discrepancies between values and performance.