Leif W. Rydstedt and Gunn Johansson, Stockholm University, and Gary W. Evans, Cornell University

The health consequences of occupational stress among male and female bus drivers were studied in
an 18-month longitudinal study. Changes in workload appeared to influence spillover of fatigue from
work to leisure, perceived effort at work, and psychosomatic symptoms at Time 2, controlling for
outcome measures 18 months previously. The potentially stress-related intake of drugs was not
affected by change in workload. No gender differences or any interactions between gender and
occupational stressors were found. Statistical controls for negative affectivity did not alter any of
these results. The importance of studying occupational stress and health among men and women
who perform the same tasks at work is discussed.

Few other contemporary professions are as stressful as urban public bus operation. Bus drivers in urban areas all over the world are exposed to a uniquely severe combination of occupational stressors. Compared to employees in comparable professions, urban bus drivers have elevated absenteeism rates (Long & Perry,
1985), retire due to disability at earlier ages (Evans, 1994), and have higher rates of psychosomatic,
cardiovascular, musculoskeletal and gastrointestinal disorders (Evans, 1994; Winkleby, Ragland, Fisher &
Syme, 1988).

Occupational stressors have been considered an important aetiological factor in health problems among
urban bus drivers. The purpose of the present article is to analyse how changes in occupational stressors
affect perceived health and well-being among male and female city bus drivers. This study is unique because it
is one of the few to analyse stress among men and women who perform exactly the same job at equal pay
rates. We also provide two important methodological contributions: the data are longitudinal and include
controls for negative affectivity.

Some of the more salient sources of occupational stress in urban transit operation include: negative social
interaction with passengers (Bartone, 1989; Duffy & McGoldrick, 1990), incessant time pressure and tightly
planned time schedules (Gardell, 1987; Reimann, 1980), and traffic congestion (Aronsson & Rissler, 1988;
Evans & Carrere, 1991).

Gardell and colleagues (Aronsson & Barklof, 1980; Gardell, Aronsson & Barklof, 1982), and Evans (1994)
have described urban bus drivers' job stressors in terms of conflicting job demands. The transit company as
well as the public demand fast and punctual transit service. Many passengers also expect personal service (e.g. information about routes, assistance to elderly or disabled passengers). Furthermore, there are compelling demands on transit operators to drive safely. The strain created by these conflicting demands is further intensified by traffic congestion and, on occasion, hazardous driving conditions.

Linkages between high workload conditions and stress reactions have been shown in many occupations
(Frankenhaeuser & Johansson, 1986; Gardell, 1987; Levi, Frankenhaeuser & Gardell, 1982). Among urban
bus drivers large elevations of blood pressure and stress hormones have been shown on the job (Aronsson &
Rissler, 1988; Evans, Palsane & Carrere, 1987). Furthermore, in a series of studies, Mulders and
collaborators (Mulders, Meijman, O'Hanlon & Mulder, 1982; Mulders et al., 1988) found that high
absenteeism drivers exhibited the greatest levels of neuroendocrine reactivity on the job. Moreover, both
Carrere, Evans, Palsane & Rivas (1991) and Gardell (1987) provide some evidence that these elevated
psychophysiological stress levels among bus drivers are workload related.

Stress, health and negative affectivity
From a methodological standpoint negative affectivity (NA) is an important concept, since it is associated with a broad range of subjective complaints and correlates as highly with physical symptom measures as these
symptoms correlate to one another. After re-examining data from a large number of studies, Watson &
Pennebaker (1989) argue that self-report measures of stress reflect a large NA component. NA is also
reflected in self-report measures of health and well-being. NA is, however, unrelated to actual physical health,
mortality, health-related behaviours or hospitalization. Therefore, studies based on self-report measures of
work environment conditions and self-report outcome measures (e.g. health measures) have the potential to
overestimate the true association between stress and health. Some, if not all, of the measured associations may emanate from spurious NA. Thus, in the present study we incorporated NA as a potential statistical control.

Gender, occupational stress and health
Few studies of occupational stress and health have examined gender differences in the same job, and none
have done so among bus drivers. Research on gender and occupational stress is in its earliest stages, with
most research comparing women's health responses to those of men across job types or even across
occupations (Frankenhaeuser, Lundberg & Chesney, 1991). Another major topic on gender and
occupational stress has been potential interactions between paid work and other activities, which may create
greater total overload in women since they frequently have greater domestic demands than men (Eckenrode & Gore, 1990; Frankenhaeuser et al., 1991).

The traditional gender work model suggests that work plays different roles in the lives of men and women.
According to this model, the work role is more important to men than to women, and consequently work
conditions generally have a more powerful impact on health and well-being for men than for women
(Loscocco & Spitze, 1990). The job model on the other hand states that gender-related differences in jobs
themselves are the only basis for gender differences in reactions to work. According to the job model male
and female workers in the same occupation, who confront the same array of job stressors, will react the same
way (Loscocco & Spitze, 1990). In reviewing a large number of studies on the consequences of family and
work roles for well-being among men and women, Barnett (1993) concludes that the two roles have the same level of importance for women and men.

An important methodological problem faced by cross-gender studies on work conditions is gender segregation in the labour market. It is difficult to find occupations where men and women perform the same work tasks. Bus driving in Sweden presents a particularly interesting situation for gender comparisons because it is an occupation that has a relatively long history of considerable female participation. Moreover, women and men share the same job demands in urban bus driving in Sweden. Labour practices and reward systems are strictly monitored, by voluntary agreement between the parties on the labour market as well as by legislation.

The purpose of Swedish equal opportunities legislation (Swedish Equal Opportunities Act, 1991) is 'to
promote females' and males' equal rights concerning work, employment and similar work conditions, and
opportunities for occupational development'. In addition, the vast majority of stressors faced by urban bus
drivers are objective, physical hassles such as traffic congestion or delays that are completely independent of
the driver. This does not, of course, preclude that male and female bus drivers may interpret and experience
their work conditions differently.

The present study
In order to verify that male and female drivers confront similar arrays of objective job stressors, we
disaggregated by gender some data from a previous study evaluating drivers' workload during a traffic
intervention in Stockholm (Rydstedt, 1996; Rydstedt, Johansson & Evans, in press). Although the intervention was motivated by economical and technical transport needs, it provided an opportunity to improve the work situation of urban bus drivers. The intervention included independent observations of on-the-job hassles, recorded while operators drove their routes.

During the days of investigation, men and women had the same frequencies of stalled traffic, road obstructions, sudden braking requirements, passengers slow in embarking and departing, delays from various sources (e.g. inquiries about bus scheduling), mechanical problems with the bus, and the bus running at full passenger capacity. For observations in one year but not the second, women were exposed more often to illegally parked cars obstructing the roadway and to other drivers behaving in a hazardous manner. Of particular interst, in the one area where stressors might be expected to interact with drivers' gender, social interactions, there were again no gender differences. Mate and female bus operators experienced the same frequencies of both positive and negative social interactions with passengers. Thus, we can state that with some confidence from these pilot data plus Swedish law and cultural practices, that male and female bus drivers in Stockholm face the same objective set of on-the-job stressors. What we do not know is whether they perceive these stressors in the same way and whether the stressors impact health similarly--that is the focus of the present study.

In the present study we compare men and women in a high-stress occupation, urban bus operation, which is
also one of the few examples of a job where men and women have identical work tasks. Furthermore, we
examine the role of negative affectivity in our analyses of workload conditions and symptoms. Moreover, as a
partial check on other variables such as self-selection, we employ a longitudinal design, examining
psychosomatic symptoms as well as spillover and effort at work at Time 2, controlling for each criterion
variable, respectively, at Time 1, 18 months earlier.

As revealed in Table 1, female participants were slightly older than male drivers, but had been employed as
bus drivers for fewer years than mate participants. The differences in age and seniority were not statistically
significant.

Procedure
The data used in the present study were collected as part of a larger project that evaluated the health effects of interventions in the traffic environment. In the present study, data from the final two waves of data collection within the longitudinal study were utilized. For further details on the questionnaire and procedures, see Rydstedt et al. (in press).

Control variable
As a measure of negative affectivity, the Positive and Negative Affect Scale (PANAS) NA scale was utilized
at Time 2 (Watson, Clark & Tellegen, 1988). This scale has high test-retest reliability over an eight-week
retest interval (r = .71), as well as high internal consistency (alpha = .87). The PANAS Scale was distributed
by post to the driver's home, about three months after the last questionnaire survey.

Independent variable
The independent variable, occupational workload, consisted of six items combined and averaged into an
index: 'time pressure', 'been forced to hurry during work', 'strained myself to keep the driving schedules', 'not
been able to give passenger service due to tight driving schedules', 'must drive too fast to keep up with time
schedules', and 'hazardous driving to keep the time schedules'. Responses referred to the preceding month and were indicated on a four-point scale: 0 = never, 1: occasionally, 2 = weekly to a few times per week, 3 = daily (alpha = .90). The effects of workload changes from Time 1 to Time 2 on Time 2 outcome measures were examined.

Dependent variables
Psychosomatic complaints during the preceding year were assessed. The index consisted of nine items: 'rapid
or arrhythmic heart activity', 'pain or constriction in the heart region or in the chest', 'breathlessness', 'pains
from upper part of the stomach/heartburn', 'gases, stomach pain, diarrhoea', 'nausea', 'headache', 'difficulties in sleeping', and 'felt more tired than before' (alpha = .86). Responses were given as frequencies: 0: never to 5 = very often, and ratings were summed and averaged.

Spillover of fatigue from work to leisure was indicated by five items: 'mental exhaustion after work', 'physical
exhaustion after work', 'difficulties unwinding after work', 'passive leisure due to exhaustion after work', and
'difficulties managing housework due to exhaustion after work' (alpha = .76). Since the items constituting this
scale initially had different response alternatives, item scores were standardized (zeta) and averaged.

Another scale was constructed to measure the consumption of pharmacological substances shown in prior
research (Jex, Hughes, Storr, Conrad, Baldwin & Sheehan, 1992; Vinet, Vezina, Brisson & Bernard, 1989)
to relate to stress and coping. This scale consisted of five items: 'tranquillizers', 'sleeping pills', 'painkillers',
'medicine for stomach problems', and 'alcohol as a tranquillizer'. Each item was reported on a five-point scale
(0 to 4) and, again, results were summed and averaged (alpha = .70).

As a final indicator of well-being, the drivers were asked to report the degree of effort required to carry out an ordinary day's work. This was done on an 11-point magnitude estimation scale, ranging from 0 ('like during a break') to 10 ('demanding to the border of exhaustion').

Results
As shown in Table 2, males reported somewhat higher negative affectivity than females, although the difference failed to reach significance. Levels of negative affectivity in the present sample are comparable to values reported by Watson, Clark & Tellegen (1988) for samples of American college students and university
employees. Statistical control for negative affectivity did not alter any of the statistically significant results that
follow.

Table 2 shows that there was only one significant difference between males and females on the outcome
variables, at Time 2. Women bus drivers reported significantly less effort on the job than their male
counterparts.

The analytic approach chosen for examination of workload demands and criteria variables was hierarchical
multiple regression with the Time 2 score of each outcome variable as criterion. Gender, workload change
(Time 2-Time 1) and their interaction were examined as predictors.[1] Table 3 shows that changes in
workload predicted perceived effort at work at Time 2, controlling for perceived effort 18 months earlier
(Time 1). Of particular interest, there was no interaction of gender and workload on perceived effort. Both
males and females reported the same levels of effort as a function of workload. The main effect of gender was
statistically significant at Time 2 (t(50) = -2.01, p < .05), but not at Time 1 (t(50) < 1, n.s.).

As revealed in Table 4, changes in workload significantly predicted spillover of fatigue from work to leisure at
Time 2. The interaction between gender and workload change was not significant, neither was the cross
sectionally analysed effects of gender at Time 1 or Time 2.

As shown in Table 5, stress-related use of pharmacological substances at Time 2 was not related to workload change or the interaction of workload change and gender after controlling for drug consumption at Time 1.

Finally, Table 6 shows that workload change was also significantly related to psychosomatic complaints at
Time 2 (after control for psychosomatic complaints at Time 1). Neither the effects of gender at Time 1 or Time 2 nor the interaction of gender and workload were significant predictors of psychosomatic complaints.

Workload was operationalized in terms of role overload and conflicting demands on the driver, in accordance
with conceptual models developed by Evans (1994) and Gardell and colleagues (Aronsson & Barklof, 1980;
Gardell et al., 1982). Negative impacts on city bus drivers' health and well-being from this type of overload
have been demonstrated, by self-reported and/or objective measures, in two previous cross-sectional studies
(Carrere et al., 1991; Gardell et al., 1982). By analysing the impact of workload upon health and well-being in a longitudinal design and with controls for negative affectivity, the present study provides stronger evidence for the negative consequences of the role overload that city bus drivers typically face. Controlling for negative
affectivity did not change the pattern or significance of relations uncovered between workload demands and
the outcome measures in this study.

No interactive effects of gender and workload demands were found in terms of health and well-being. Thus,
our findings are consistent with findings by Loscocco & Spitze (1990) and Shinn, Rosario, Morch & Chestnut (1984). When men and women are exposed to stressors which are objectively identical, they tend to react in the same way. Moreover in the present case, they cognitively appraise their workload levels similarly. However, considering that few employed women--especially in the blue collar workforce--are exposed to the same occupational stressors as are employed men, the present sample of female bus drivers may not be representative of women in general. Thus a more conservative conclusion is warranted. Women working in non-traditional professions exhibit reaction patterns similar to those of males. Our findings are also in line with Frankenhaeuser's (1991) assumption that gender differences in stress reactions are primarily determined by biopsychological rather than by purely biological mechanisms, and with an interactionistic approach, assuming that gender differences result from situation as well as person factors (Magnusson & Allen, 1983).

Our findings support the 'job model' of reactivity to occupational stress proposed by Loscocco & Spitze
(1990), rather than the 'gender model'. It should be noted that our study, as Loscocco & Spitze's (1990) and
Shinn et al.'s (1984), only deals with reactions to the more direct and concrete sources of occupational
stress. We have not examined conflicting role demands, nor has it been possible to take into account possible
gender interactions with total workload generated by paid and unpaid work.

Finally, a word of caution. The limited sample size may bias our results in favour of no gender difference. Yet,
despite the limited size of our sample, there was sufficient sensitivity for detection of the workload effect.
Moreover, the repeated measures design substantially augments statistical power. The issue of sample
representativeness remains open, but few other studies--whether of large or small sample size--have had the
opportunity, in a longitudinal perspective, to compare male and female blue-collar workers performing the
same task (cf. Zapf, Dormann & Frese, 1996). Cross-sectional studies addressing the gender issue, few as
they are, have been based on sample sizes relatively similar to this study (Collins, 1985; Forsman & Lundberg, 1982; Frankenhaeuser, Lundberg, Fredriksson, Melin, Tuomisto & Myrsten, 1989; Johansson & Post, 1974). Nevertheless, more multimethod, longitudinal studies with larger samples of men and women
performing the same tasks are necessary to more firmly support the job versus gender model of sex
differences in occupational stress and health.

We conclude that changes in workload demands among urban bus operators are related to spillover of fatigue from work to leisure, perceived effort at work, and psychosomatic complaints. Statistical control for negative affectivity does not alter these results. Moreover, there are no interactive effects of gender and workload. Blue-collar women and men in the same, high stress occupation appear to react to changes in job stress over time in a similar manner.

                               Men (N = 35)       Women (N = 17)

Variable                        M        SD         M        SD

Age (years)                   41.4      10.7      44.5       7.6
Seniority (years)             12.1       9.3       7.9       6.3

Table 2. Average affectivity and self-reported effort, fatigue spillover from work to leisure, drug use and
psychosomatic complaints for male and female bus drivers at Time 2 and the difference in self-reported
workload between Time 2 and Time 1

Legend for Chart:

A - Variable
B - Men (N = 35): M
C - Men (N = 35): SD
D - Women (N = 17): M
E - Women (N = 17): SD
F - t

A                                                     B     C      D     E        F

Negative affectivity                         18.0   5.2   14.7   6.2     1.95
Workload diff. (Time 2-Time 1)       0.0   0.6   -0.1   0.4    -0.51
Perceived effort                               5.1   1.8    4.6   2.6 -2.01[a]
Work spillover (zeta score)              0.0   0.7   -0.3   0.7    -1.55
Intake of drugs                                0.5   0.6    0.5   0.7    -0.05
Psychosomatic complaints               1.3   0.8    1.1   0.9    -0.99

a p < .05.

Table 3. Multiple regression analysis: Gender, residualized workload, and their interaction to perceived effort
at work at Time 2. Control for perceived effort at Time 1

Legend for Chart:

A - R[sup 2]
B - F(R[sup 2])
C - deltaR[sup 2]
D - F(deltaR[sup 2])
E - beta

                                             A       B            C       D        E

Perceived effort Time 1        .55    57.8[d]   .55   57.8[d]    .74
Gender (G)                         .57    31.5[d]   .03       2.9   -.16
Workload A score (W)       .68    32.9[d]   .11   15.8[c]    .33
G x W                                 .68    24.1[d]   .00      <1.0   -.01

c p < .001; d p < .0001.

Table 4. Multiple regression analysis: Gender, residualized workload, and their interaction to spillover of
fatigue at Time 2. Control for fatigue spillover at Time 1

Legend for Chart:

A - R[sup 2]
B - F(R[sup 2])
C - deltaR[sup 2]
D - F(deltaR[sup 2])
E - beta

                                             A        B           C       D            E

Fatigue spillover Time 1       .68   110.0[d]   .68  110.0[d]    .82
Gender (G)                         .69    54.7[d]     .00     < 1.0    -.05
Workload delta score (W)   .76    50.5[d]   .07   13.9[c]      .27
G x W                                 .76    38.3[d]   .01       1.2       -.24

c p < .001; d p < .0001.

Table 5. Multiple regression analysis: Gender, residualized workload, and their interaction to intake of
stress-related drugs at Time 2. Control for intake of drags at Time 1

Legend for Chart:

A - R[sup 2]
B - F(R[sup 2])
C - deltaR[sup 2]
D - F(deltaR[sup 2])
E - beta

                                           A       B           C        D          E

Intake of drugs Time 1       .64   89.4[d]   .64   89.4[d]     .80
Gender (G)                       .64   44.2[d]   .00     < 1.0    -.05
Workload A score (W)     .64   28.9[d]   .00     < 1.0     .00
G x W                               .64   21.2[d]   .00     < 1.0    -.03

d p < .0001.

Table 6. Multiple regression analysis: Gender, residualized workload, and their interaction to psychosomatic
complaints at Time 2. Control for psychosomatic complaints at Time 1

Legend for Chart:

A - R[sup 2]
B - F(R[sup 2])
C - deltaR[sup 2]
D - F(deltaR[sup 2])
E - beta

                                          A        B           C        D        E
Psychosomatic
  complaints Time 1           .77   174.6[d]   .77  174.6[d]  .88
Gender (G)                       .78    89.0[d]   .01      1.56    -.08
Workload A score (W)     .80    65.5[d]   .02   4.12[a]     .13
G x W                              .80    48.3[d]   .00     < 1.0     .18

a p < .05; d p < .0001.

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Source: Journal of Occupational & Organizational Psychology, Mar98, Vol. 71 Issue 1, p35, 11p, 6 charts.