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ORIGINAL RESEARCH
Determinants of Depressive Symptoms Among
Rural Health Workers: An Application of
Socio-Ecological Framework
This article was published in the following Dove Press journal:
Journal of Multidisciplinar y Healthcare
Fatemeh Bakhtari
1
Parvin Sarbakhsh
2
Jalil Daneshvar
1
Devender Bhalla
3,4
Haidar Nadrian
5
1
Department of Health Education and
Promotion, Faculty of Health, Tabriz
University of Medical Sciences, Tabriz,
Iran;
2
Department of Statistics and
Epidemiology, Faculty of Health, Tabriz
University of Medical Sciences, Tabriz,
Iran;
3
Pôle Universitaire euclide
Intergovernmental UN Treaty 49006/
49007, Bangui, Central African Republic;
4
Iranian Epilepsy Association, Tehran, Iran;
5
Social Determinants of Health Research
Center, Tabriz University of Medical
Sciences, Tabriz, Iran
Objective: The objective of this study was to assess depressive symptoms among rural
health workers (RHWs) through a multi-factorial socio-ecological framework (SEF) encom-
passing personal, interpersonal, organizational and community components.
Patients and Methods: A random sample of 394 RHWs in all rural areas of East
Azerbaijan and fullling our other inclusion criteria were recruited. The participants under-
went the Short-Form Beck’s Depression Inventory and a validated researcher-constructed
SEF questionnaire, including subscales on personal, interpersonal, organizational and com-
munity factors associated with depressive symptoms. Internal consistency and factor struc-
ture parameters of the SEF were also calculated.
Results: A total of 394 RHWs were screened, of whom 170 (43.2%) had mild to major
depressive symptoms. Only 6.8% were identied with major depressive symptoms. The SEF-
based scale was found to have acceptable content validity (content validity index and ratio
were 0.80 and 0.77, respectively) and reliability (Cronbach’s alpha=0.7). In the structural
equation modeling, the t indices showed our model to t the data well (χ
2
=14.06, df=14, χ
2
/
df=1.00, CFI=0.967, RMSEA=0.032). The highest direct contribution to depressive symp-
toms was found from the personal factors component (β=−2.32). Also, “work load and roles
interference” (from organizational level, β=−0.76) and “family/colleague support” (from
community level, β=−1.28) made signicant direct contributions towards depressive symp-
toms. Besides the SEF components, female gender (β=1.69), family history of mental illness
(β=−1.48), having chronic illnesses (β=−1.64) and being religious (β=3.43) were the stron-
gest direct contributors to depressive symptoms.
Conclusion: Depressive symptoms were common among RHWs, arising from all personal-,
interpersonal-, organizational- and community-level factors. Our SEF had adequate internal
consistency and factor structure parameters to be applied in the Middle East and North Africa
(MENA) region countries, such as Iran, as a theoretical framework to plan for interventional
efforts aiming at preventing depressive symptoms among RHWs. The burden of depressive
symptoms should be reduced through multi-factorial interventions and rational perspectives.
Keywords: depression, rural healthcare, socio-ecological framework, rural health workers
Introduction
Depression is a public health concern and one of the most common psychiatric
disorders.
1,2
Depression is reported as the most important cause of disability and
insufciency, with decreased levels of willingness to work and take action as one of
its major complications.
3–5
Depressive symptoms are reported to be common
among health and medical staff.
3,6–9
Among depressed staff, absenteeism and job
Correspondence: Haidar Nadrian
Department of Health Education and
Promotion, Faculty of Health,
Tabriz University of Medical Sciences,
Ofce No. 317, Attar-e-Neyshabouri St.,
Tabriz 6617777541, Iran
Email haidarnadrian@gmail.com
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switching,
10
as well as loss of productive time, are higher
than in their non-depressed counterparts.
11
The burden is
far more severe among healthcare providers in settings in
the Middle East and North Africa (MENA),
4,6,7
where the
overall frequency of depression is far higher
3,12-14
than
elsewhere.
About 45.0% of the population in the MENA region is
rural. In such rural settings, rural healthcare workers (RHWs)
are the primary healthcare workforce, who provide a broad
multi-faceted service to the population they are closest to.
15
In Iran, RHWs provide the rural populations with primary
health care (PHC) and play a pivotal role in promoting the
health of rural communities. Although they provide care to
the other members of the public, they are not immune to poor
mental health in general and mental health issues such as
depressive symptoms in particular.
3–5
For instance, about
70.0% of primary healthcare workers are likely to be affected
by depressive symptoms.
6
By being affected themselves,
they may not provide adequate help to the patients,
6,7
which also reects negatively on the patients’ health.
3,13
Depression cannot occur in isolation to one’s environ-
mental factors.
16,17
Thus, taking into account those factors
that are innately related to home, immediate family, rela-
tives, workplace, peers, co-workers, children’s school,
neighbors, culture and the community in which RHWs live
is essential. These factors cannot be posited together under
personal variables
17
as they, rather, represent a wide range of
inuences at multiple levels within what is known as
a socio-ecological framework (SEF) (Figure 1). The World
Health Organization (WHO) also agrees to a multi-
directional complexity and dynamic interaction between
a wide range of pathogenic and salutogenic factors under
its bio-psycho-social model of health.
3
Similarly, others
have pointed out that individuals are “more than their mere
illness”.
18
In the present study, we assumed that a broad
range of multilayered personal, cultural and environmental
factors may be associated with depressive symptoms among
RHWs. Therefore, in order to nd a better understanding
with a broader perspective on the various determinants of
depressive symptoms, we decided to apply the SEF. This
framework may advance the health promotion programs
from focusing on changes on a behavioral or intrapersonal
level to a broader range of changes in the social and envir-
onmental context related to behavior and health-related
issues.
19
In order to improve the health of populations,
there is a need to investigate multiple levels of inuence.
20
Based on the SEF, health and behavior are the outcomes
of interest,
21
which are determined by the factors from
personal and interpersonal levels to organizational, social
and political levels.
22
Personal-level factors are the charac-
teristics of an individual, including knowledge, attitudes,
self-concept and skills. Interpersonal factorsare social sup-
port systems and formal/informal social networks, including
family, friendship and work group networks. Organizational-
level factors are institutional characteristics associated with
organizations and their formal/informal operation, rules and
regulations, including nancial policies and workplace cli-
mate (tolerance/intolerance). Community-level factors
Figure 1 Socio-ecological framework.
Note: Bronfenbrenner U,Toward an experimental ecology of human development, American Psychologist Association, 32, 7, 513, 1977, reprinted with permission from APA.
77
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include the characteristics related to the location in the com-
munity, housing, culture, neighborhood associations, built
environment, community leaders and transportation. Policy-
level factors are local, national and/or international laws and
policies that serve as a mediating structure to connect people
and the larger social environment to make healthy choices,
e.g. increased taxes on cigarettes and alcohol, and policies
related to social injustice and global warming.
23–25
The SEF has widely been used to approach different
health problems.
19,26-29
Worldwide, there are only partial
uses of this framework in depression.
30,31
Smokowski
et al, applying a socio-ecological approach, investigated
individual-, social- and school-level characteristics asso-
ciated with depressive symptoms and self-esteem among
a sample of US rural youth, and found that having a low
income, being female, and having negative relationships
with parents and peers were risk factors for higher levels
of depressive symptoms.
31
In another study, Olson and
Goddard applied the SEF to determine the factors asso-
ciated with depressive symptoms among US adolescents,
and reported several protective factors that directly con-
tributed to lower levels of depressive symptoms.
32
As far
as we are aware, no study has previously evaluated depres-
sion and depressive symptoms among RHWs in a non-
Western context using a multi-dimensional SEF.
20,22,27
Thus, with such a vision, we conducted a population-
based assessment of depressive symptoms among those
working as RHWs in East Azerbaijan, Iran, using the
SEF. The following questions guided our study:
1. What is the pattern of depressive symptoms among
RHWs in Iran, as a developing country?
2. What are the personal-, interpersonal-, organiza-
tional- and community-level factors contributing to
depressive symptoms among RHWs?
3. What are the direct and indirect contributions of the
SEF-based factors towards depressive symptoms
among RHWs?
4. Could the SEF be used in a MENA region country,
such as Iran, as a theoretical framework to plan for
interventional efforts aiming at preventing depres-
sive symptoms among RHWs?
Methods
Study Design and Participants
In this cross-sectional study, we aimed to determine the
predictors of depressive symptoms among RHWs in East
Azarbaijan province, Iran. In this developing country,
healthcare services in rural areas are delivered by rural
health care centers (RHCCs), which cover some health
houses (HHs) in proportion to the population under their
coverage. In this healthcare system, HHs are the rst level
of contact with rural and remote populations, and provide
rural communities with a wide range of PHC services, e.g.
maternal and child health, health education, family plan-
ning, disease surveillance and prevention, environmental
health and healthy nutrition education. Depending on the
geographical situations, population of the villages and
communication facilities, one to three RHWs are
employed in every HH.
In 2017, multi-stage random sampling was employed
to recruit 394 RHWs. As the rst step, the province was
divided into four separate regions: north, south, east and
west. Then, one county was randomly selected from each
region (in total four counties were selected). In the third
step, 300 HHs were again randomly selected from all four
counties. In total, 421 RHWs were employed in the 300
HHs, within which 200 HHs had two RHWs and 21 HHs
had only one RHW, as personnel. In the Iranian health
system, only one or two RHWs are employed in each rural
HH. Finally, all RHWs in the selected HHs were invited to
participate in the study. The estimation of 10 samples per
item was considered to determine sample size for applying
structural equation modeling (SEM).
33
As the number of items was 37 and considering an
attrition rate of 10%, the sample size was estimated to
be 407. Fourteen respondents declined to participate in
the study (response rate = 96.6%). Also, the informa-
tion for 13 respondents was not included in the analy-
tical process, owing to missing data. Finally, the data
on 394 RHWs were included in the data analysis. All
respondents were invited to participate in the study;
before providing them with the questionnaire, they
were informed about the aim of study and assured on
the condentiality of data, and, nally, they all signed
a consent form. The RHWs with more than 1 year of
work experience in the current job, with no close
relatives having died in the previous year and with no
history of severe mental disorder in the family were
included in the study. The RHWs who were being
treated with anti-depression medications (as we pre-
sumed that the answers for such cases, because of
medication, might bias the answers of all those who
do not use any anti-depression drugs) and those who
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969
refused to complete the questionnaire were excluded
from the study.
Measures
A researcher-constructed demographic and underlying data
form with seven items was used to obtain data related to
age, level of education, saying prayers (Yes/No), reading
religious texts/words (the Quran, prayers and so on) (Yes/
No), current smoking (smoking at least one cigarette
per day) (Yes/No), history of childbirth during the pre-
vious 3 years (Yes/No) and being menopausal in the pre-
vious 3 years (for female RHWs only) (Yes/No). These
last two questions were asked to identify those who may
be at risk for postnatal depression and depression at meno-
pause, respectively.
The Beck’s Depression Inventory – Short Form (BDI-13-
SF)
34
was used to measure the level of depressive symptoms
among the respondents. The Persian version of this inventory
is validated in Iran.
35
BDI-13-SF is a 13-item standard self-
report questionnaire used to classify the severity of depres-
sion symptoms in the following four categories: normal
(0–4); mild depression (5–7); mild to moderate depression
(8–15); and severe depression (16–39). The items are scored
on a four-point basis from 0 to 3. The total score for the scale
ranges from 0 to 39.
After a review of literature,
36–40
a pool of factors
associated with depressive symptoms at various work-
places was prepared. The research team selected the most
relevant factors and then, by applying the socio-ecological
approach, classied them into three levels: personal-, orga-
nizational- and community- level factors. The factors in
the three levels were considered as the basis on which to
develop the questionnaire.
Personal-Level Factors
To investigate the factors at the personal level,
a researcher-constructed questionnaire including four
items was developed. This questionnaire included “level
of interest in job”, “level of satisfaction with job”, “ability
to do things with current literacy” and “perception on the
level of success in the eld of work”. For all four items,
a ve-point Likert-type scale was used as the response
format (none=1, low=2, moderate=3, high=4 and very
high=5). The scores of respondents on the four items
were summed to nd a nal score for the personal-level
factors.
Organizational-Level Factors
Eight items were also developed to investigate the factors
at the organizational level: having work load (two items),
level of monitoring and evaluation (two items), reinforcing
factors (two items) and satisfaction with the organization
(two items). The scores of the respondents on the eight
items were also summed to nd a nal score for the social-
level factors. For all three levels, the items were rated on
a ve-point Likert-type scale ranging from 1 to 5 (1=none
to 5=very high).
Community-Level Factors
Twelve items were developed to investigate the factors at
the community level. This researcher-constructed scale
included ve subscales: relationships with colleagues,
family and others (three items), perceived social support
from colleagues, family and others (three items), perceived
cultural and environmental barriers in the village (two
items), facilities in the village (two items), and enjoyment
from living and working in the village (two items). The
scores of respondents on the 12 items were summed to nd
a nal score for the community-level factors.
Content Validity
The content validity of the instrument was qualitatively
assessed by an expert panel including 11 specialists in the
elds of psychology, psychiatry, health education and pro-
motion, and experts from a health worker training school.
The responses from the expert panel were used to alter
and/or modify the items. The content validity index (CVI)
was measured. A CVI score higher than 0.75 was consid-
ered as acceptable.
41
The content validity ratio (CVR) was
also calculated. The scales with CVRs equal to or higher
than 0.59 were considered to have good levels of content
validity. The CVI and CVR for the scale were 0.85 and
0.77, respectively. In order to conduct a preliminary test on
internal consistency, and to assess ambiguity and clarity,
the scales were then pilot tested among 35 RHWs, who
were not included in our nal sample.
Reliability
The internal consistency reliability of the scale was
approved after calculating Cronbach’s alpha in our pilot
(α=0.66) and nal (α=0.70) samples. Also, the Spearman–
Brown coefcient was used to assess the stability of the
scale over time in the nal sample (r=0.72).
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Ethical Approval
Ethical approval for this study was provided by the
research committee in an Iranian Medical Sciences
University (Ethic Code: IR.TBZMED.REC.1396.284).
Statistical Analysis
Data are presented as mean (standard deviation; SD) and
frequency (percent) for quantitative and qualitative vari-
ables, respectively. In order to compare the demographic
and underlying variables between the groups with and
without depressive symptoms, the independent sample
t-test and χ
2
-test were used.
Construct Validity
To assess the factor structure of the scale, exploratory
factor analysis (EFA) was conducted, applying principal
component factor analysis with varimax rotation, using the
randomization function on SPSS version 22. The factor
loadings greater than or equal to 0.3 were considered as
appropriate, and eigenvalues above 1 were the bases for
assigning the number of factors. The Kaiser–Meyer–Olkin
(KMO) test and Bartlett’s test of sphericity were applied to
determine the appropriateness of the sample.
Structural Equation Modeling
Considering the SEF approach and on the basis of the
EFA results, the levels of factors inuencing depressive
symptoms were categorized into four levels: personal,
interpersonal, organizational and community. SEM with
identity link function and maximum likelihood estima-
tion was applied to investigate the relationships between
the variables, and the direct effects (dealing with the
direct impact of an SEF factor on the dependent vari-
able, depressive symptoms, when not mediated through
a third factor) and indirect effects (the impact of an SEF
factor on the dependent variable, depressive symptoms,
mediated by a third factor) of the factors at different
levels on the depression score. Stata software, version
14, was applied to test the t of the determinant model
of depressive symptoms to the data. As our aim was to
determine the relationships between the socio-ecological
factors at four levels and depressive symptoms based on
SEF, all levels and depression were considered as
observed variables and thus the SEM analysis was con-
ducted as a path analysis. The path coefcients and
correlations were reported as standardized estimates.
Two primary tests were conducted to survey the data
t. The practical indicators of t, according to conr-
matory factor analysis, included chi-square, χ
2
/df, root
mean square error of approximation (RMSEA) and com-
parative t index (CFI).
The values for the CFI range from 0 to 1 and are derived
from comparisons between a hypothesized model and the
independent model; a value greater than 0.90 indicates an
acceptable t to the data. Conventionally, there is a good
model t if the RMSEA is less than or equal to 0.08 and the
root mean square of the residuals (RMSR) is less than 0.05.
There is also adequate t if the RMSEA is less than or equal
to 0.08 and the RMSR is less than 0.05.
42
The level of
signicance was considered to be less than 0.05, a priori.
Results
Participants
A total of 394 RHWs were screened, of whom 170 sub-
jects (43.5%) were found to have mild to major depressive
symptoms. The socio-demographic details in association
with depressive symptoms are provided in Table 1. The
mean±SD age of participants was 40.86±6.53 years. Of
those affected, 22.9% were men, although there was no
statistically signicant gender difference in the frequency
of depressive symptoms (p=0.26) (Table 1).
Factor Structure
In the EFA, the KMO measure of sampling adequacy for
the scale was 0.838 (approximate χ
2
=2647.13, df=276,
p≤0.001). In the last iteration of EFA, seven distinctive
factors were extracted as the best solution, which
together explained about 59.7% of the total variance
between the items. The list of factors loaded (subscales),
their range and number of items, mean and standard
deviation, oor and ceiling effects, as well as kurtosis
and skewness of the factors, are shown in Table 2. Table
3 shows the rotated factor pattern coefcient for the
seven-factor solution. The research team then compared
this solution with the SEF model and found that it to
matched the theoretical framework. Then, after
a consultation with the initial panel of experts, the
seven factors were categorized into the four levels of
the SEF, namely personal, interpersonal, organizational
and community levels (Table 4). In summary, the aver-
age alpha coefcient of our entire SEF was about 0.70,
with different mean scores for each of its three compo-
nents for different degrees of depressive symptoms
(Table 4). Furthermore, the overall CVI and CVR for
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971
SEF items were 0.85 and 0.77, respectively. The
Spearman–Brown coefcient, used to assess the stability
of SEF over time, was estimated to be 0.72.
Differences in the SEF Factors by
Depressive Symptoms
Levels of difference in the SEF factors according to dif-
ferent degrees of depressive symptoms among RHWs are
displayed in Table 4. Of all those affected (n=170), 15.8%
were found to have major depressive symptoms (Table 4).
Minor, moderate and major depressive symptoms were
found in 16.4%, 19.7% and 6.8% of participants, respec-
tively. Applying one-way ANOVA, signicant differences
were found in all seven domains of the factors (categor-
ized in all four levels of SEF) with respect to depressive
symptoms among RHWs. The only exception was for the
“reinforcing factors”. The highest and the lowest scores
were found to be for “interaction with villagers” at the
Table 1 Associations Between Demographic Characteristics and Depressive Symptoms Among Rural Health Workers
Variable Total (N=394) Mild–Major Depressive Symptoms (N=170) Normal p
Age (mean±SD) 40.86±6.53 42.01±5.74 39.98±6.95 0.002
Gender, n (%)
Male 103 (100) 39 (37.9) 64 (62.1) 0.26
Female 291 (100) 131 (45) 160 (55)
Marital status
Single 30 (100) 9 (30) 21 (70) 0.13
Married 364 (100) 161 (44.2) 203 (55.8)
Family history of mental illness
Yes 62 (100) 41 (66.1) 21 (33.9) 0.000
No 331 (100) 128 (38.7) 203 (61.3)
Saying daily prayer
Yes 385 (100) 166 (43.1) 219 (56.9) 0.93
No 9 (100) 4 (44.4) 5 (55.6)
Belief in oneself as a religious person
Yes 371 (100) 156 (42) 215 (58) 0.07
No 23 (100) 14 (60.9) 9 (39.1)
Educational level
Elementary 57 (100) 31 (54.4) 26 (45.6) 0.22
Secondary 82 (100) 42 (51.2) 40 (48.8)
High school 196 (100) 79 (40.3) 117 (59.7)
University education 59 (100) 18 (30.5) 41 (69.5)
Smoking
Yes 20 (100) 10 (50) 10 (50) 0.52
No 374 (100) 160 (42.8) 214 (57.2)
Having had a childbirth in the previous 3 years
Yes 42 (100) 21 (50) 21 (50) 0.48
No 249 (100) 110 (42.2) 139 (55.8)
Having had menopause in the previous 3 years
Yes 9 (100) 8 (88.9) 1 (11.1) 0.007
No 282 (100) 123 (43.6) 159 (56.4)
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interpersonal level (7.78 out of 10) among non-depressed
RHWs and “reinforcing factors” at the organizational level
(3.26 out of 10) among RHWs with major depressive
symptoms, respectively. Also, Tukey’s HSD test showed
that non-depressed RHWs had signicantly greater mean
(SD) scores in “personal factors”, “interaction with collea-
gues/monitoring and evaluation”, “culture and environ-
ment of village” and “family/colleague support”
compared to the RHWs with moderate and severe levels
of depressive symptoms. Non-depressed RHWs were also
found to have higher mean scores on “work load and roles
interference”, compared to those with low and moderate
levels of depressive symptoms.
Structural Equation Modeling
The t indices showed that our model tted the data
well (χ
2
=14.06, df=14, χ
2
/df=1.00, CFI=0.967,
RMSEA=0.032). The highest direct contribution to
depressive symptoms was found with the “personal fac-
tors” component (β=−2.32), which also made the highest
total contribution towards depressive symptoms (β=
−2.26). Among other factors of SEF, “work load and
roles interference” (from the organizational level) and
“family/colleague support” (from the community level)
made both signicant direct (“work load and roles inter-
ference”, β=−0.76; “family/colleague support”, β=−1.28)
and total (“work load and roles interference”, β=−0.86;
“family/colleague support”, β=−1.38) contributions
towards depressive symptoms (Table 5 and Figure 2).
In addition, we found that “belief in oneself as
a religious man/woman” (β=3.43), family history of
mental illness (β=−1.48), gender (β=1.69), educational
level (β=−0.56), income status (β=−0.84) and having
chronic illness (β=−1.64) directly contributed to depres-
sive symptoms (Table 5 and Figure 2). The effects of
personal, interpersonal, organizational and community
components on depressive symptoms are displayed in
Figure 2.
Discussion
The aim of this study was to investigate the factors asso-
ciated with depressive symptoms among Iranian RHWs by
applying the SEF of health in East Azerbaijan province,
Iran. RHWs are at the bottom of the healthcare delivery
system, and are more prone to poor mental health than
other medical workers because of poorer remuneration,
and fewer occupational, reward, relocation, professional
support and training opportunities.
43
RHWs also need to
make frequent eld visits without adequate transport facil-
ities and are likely to be overburdened,
4
which may affect
their recreational and socializing opportunities as well as
their family environment. As one example, the monthly
wage of RHWs is merely one-sixth of that of other health-
care providers in Iran.
44
Elsewhere in MENA, the com-
pensation of similar health workers is likely to be far
higher.
45
Depressive symptoms were present in 43.2% of our
subjects, and although this is an unacceptable number,
major depressive symptoms were present in only 6.8% of
our overall sample population and 15.8% of the affected
subjects. A direct comparison with other studies is unsui-
table given the possibility of methodological differences,
Table 2 Characteristics of the Factors Derived from Exploratory Factor Analysis
Factor (Subscale) Number of
Items
Range Mean
(SD)
Kurtosis Skewness Floor
Effect (%)
Ceiling
Effect (%)
F1: Personal factors 5 5–25 18.55 (3.8) 0.416 −0.548 0 0.2
F2: Interaction with villagers 2 2–10 7.46 (1.4) −0.335 −0.554 4.4 1.3
F3: Interaction with colleagues/monitoring
and evaluation
4 4–20 13.84 (2.2) 0.063 −0.421 0 0.2
F4: Culture and environment of village 6 6–30 16.8 (2.7) −0.169 0.191 0.4 6.3
F5: Reinforcing factors 2 2–10 3.62 (0.81) 2.1 1.0 0 0.2
F6: Family/colleague support 3 3–15 11.46 (1.9) 0.29 −0.64 0.7 7.1
F7: Work load and roles interference 2 2–10 5.78 (1.04) 0.37 0.35 0.3 2.1
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Table 3 Rotated Factor Pattern Coefcients for Variable Solutions (24 Items) of the SEF Factors
Factors* F1** F2 F3 F4 F5 F6 F7
a2 Do you have the necessary skills (like communication skills and practical
skills) to perform the RHW tasks?
0.809
a4 In your opinion, are you a useful and valuable staff in the health system? 0.789
a3 Considering your current job abilities and literacy, can you perform the
new circulars, announced by the health ministry?
0.758
a1 Are you interested in your job as a RHW? 0.700 −0.324 −0.345
d1 Do you feel success in your job, as a RHW? 0.673 −0.481 −0.344
c1 Do the customs and culture in the village impede you in developing
satisfactory relationships with people while performing your tasks?
0.798
b3 During formal/informal relationships, do the villagers cause discomfort
for you?
0.737 0.304
d3 Does the monitoring of your supervisors improve your abilities? −0.846
d4 Are you satised with the mode of supervisors’ guidance and instruction
while monitoring sessions?
−0.821
d5 Are your job activities supervised based on previously delivered
checklists?
0.340 −0.679
b1 Are you glad from being with your colleagues? 0.380 −0.514 −0.305 −0.365
c3 Do you like the village you are working in? 0.373 −0.381 −0.758
c5 Do you enjoy living in the village? −0.356 −0.735
c4 Do you like the customs and the culture of the village you are working
in?
0.331 0.364 −0.316 −0.723
c2 Do the facilities existing in the village meet your living needs? −0.633
c8 When dealing with problems, do the villagers support you? −0.508 0.416 0.495
d8 Are you satised with the physical environment of your health house (in
terms of size, temperature, equipment and so on)?
−0.470
d6 Have you ever been encouraged for your efforts by your boss? 0.712
d7 Has your organization ever implemented recreational and welfare
programs for you?
0.709
c6 When dealing with problems, does your family support you? 0.354 0.805
b2 Does your family cause discomfort for you? 0.687
c7 When dealing with problems, do your colleagues support you? −0.387 −0.428 0.663
d2 Are you overloaded with your job responsibilities? 0.692
c9 Has your job caused you to not play well your other roles (like your
mother/father role or your husband/wife role) in your life?
0.634
Initial eigenvalues 5.67 2.14 1.52 1.45 1.29 1.21 1.03
Rotation sums of squares 3.03 2.78 2.35 1.86 1.67 1.33 1.28
Percent of variance explained 23.63 8.92 6.35 6.08 5.38 5.06 4.31
Cronbach’s α0.809 0.65 0.72 0.74 0.51 0.61 0.49
Notes: *F1: Personal factors, F2: Interaction with villagers, F3: Interaction with colleagues/monitoring and evaluation, F4: Culture and environment of village, F5: Reinforcing
factors, F6: Family/colleague support, F7: Work load and roles interference. Extraction method: principal component analysis; rotation method: varimax with Kaiser
normalization. **The greatest factor loadings are shown in bold.
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but our overall frequency of depressive symptoms was
lower than,
46,47
similar to
4
and higher than
12
those
reported by others. In two studies applying the BDI ques-
tionnaire, 10.9% of Brazilian intensive care nurses
48
(n=91) and 10.5% of Swedish men in a primary care
unit
49
(n=223) had symptoms of depression. In another
study among 2798 students using the same scale in
India,
50
depressive symptoms were reported among
40.2%, 38.5% and 47.2% of engineering, dentistry and
medical students, respectively. The reason for such study-
to-study disagreement may be related to the type of ques-
tionnaire, study setting, nature of participants and study
design applied.
4,12
For instance, the study that reported
higher levels of depressive symptoms than ours recruited
its participants from healthcare homes belonging to
a single university and used a different questionnaire, i.e.
the Patient Health Questionnaire (PHQ-9) for depression
than ours. In a randomized trial,
51
the BDI, which we
used, was reported to have better psychometric properties
and factor structure parameters than PHQ-9. Moreover, the
BDI questionnaire provided a greater proportion of sub-
jects with major depression than PHQ-9, which was true
for us as well. Nevertheless, another study that was con-
ducted among RHWs and used the same questionnaire as
ours found nearly an identical frequency of depressive
symptoms (43.4%),
4
even though the sample size was
considerably smaller than ours.
Our high level of depressive symptoms, and of major
depression,
14
reinforces that RHWs are possibly more
prone to depressive symptoms than other medical workers,
both in Iran
7
and elsewhere.
3,8
Their susceptibility to
depressive symptoms should not be unexpected, given
their inferior situation, regarding to their family, social
and occupational life,
52
for themselves and their spouse
and children.
13,53
For instance, the majority of RHWs
(69%) reported that they are not interested in their job as
an RHW. Moreover, the proportion of major depressive
symptoms among RHWs was similar to that observed
among truck drivers.
54
This should also be not surprising,
for many reasons. For instance, RHWs can change their
place of residence but cannot change their place of work,
placing them against the theory of locus of control, which
is closely linked to the risk of depressive symptoms.
55
Moreover, the relocated RHWs have to commute
a certain distance daily, in addition to the eld travel that
they have to do as part of their usual work, often without
adequate transportation facilities, which may lead to
burnout.
56
According to previous studies, 30–40% of
healthcare workers may suffer from burnout.
57–59
The topic of depression is fairly broad. Nevertheless,
our SEF sought to be comprehensive by capturing the
necessary risk-related information peculiar to the ecology
of RHWs at various levels of inuence (Figure 2). For
instance, our SEF examined individual-level factors (e.g.
Table 4 Socio-Ecological Framework (SEF) by Different Degrees of Depressive Symptoms Among Rural Health Workers
Level of SEF Factors/Constructs Beck’s Depression Inventory (BDI-13)-Short Form p
Total,
M (SD)
Non-
Depressed,
M (SD)
Minor,
M (SD)
Moderate,
M (SD)
Major,
M (SD)
N=394
(100%)
N=224 (56.8%) N=65
(16.4%)
N=78
(19.7%)
N=27
(6.8%)
Personal
factors
Personal factors 18.55 (3.8) 19.6 (3.9) 18.45 (3.7) 16.9 (3.2) 14.85 (4.1) ≤0.001
Interpersonal
factors
Interaction with villagers 7.46 (1.4) 7.78 (1.6) 7.5 (1.3) 6.64 (1.1) 7.02 (1.2) ≤0.001
Organizational
factors
Interaction with colleagues/
monitoring and evaluation
13.84 (2.2) 14.44 (2.4) 13.88 (2.2) 12.72 (1.7) 12.08 (1.4) ≤0.001
Reinforcing factors 3.62 (0.81) 3.72 (0.89) 3.66 (0.85) 3.26 (0.64) 3.62 (0.84) =0.102
Work load and roles interference 5.78 (1.04) 5.3 (0.9) 4.64 (0.7) 4.5 (0.6) 4.66 (0.7) ≤0.001
Community
factors
Culture and environment of village 16.8 (2.7) 17.64 (2.9) 17.52 (2.7) 15.06 (1.4) 13.62 (0.91) ≤0.001
Family/colleague support 11.46 (1.9) 11.58 (2.1) 10.95 (1.7) 9.69 (1.4) 8.25 (1.1) ≤0.001
Abbreviations: M, mean; SD, standard deviation.
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975
gender and educational level) and personal-level factors
(e.g. having the necessary skills to perform the tasks) that
increase the likelihood of becoming a victim; interpersonal
factors (e.g. interaction with villagers) that may increase
the risk of experiencing depressive symptoms as a victim;
organizational factors (e.g. work load and roles and inter-
action with colleagues) to identify the characteristics of
these settings that are associated with becoming victims of
depression; and broad community factors (e.g. culture and
environment of village and family/colleague support) that
provide a broad climate in which one may become
a victim of depressive symptoms or remain protected. In
addition, the SEF-based scale showed acceptable levels of
content and construct validity, and our SEF had high t
indices and adequate alpha coefcients, which all imply
that the seven components and their items tted well for
the assessment of depressive symptoms.
The highest direct contribution to depressive symptoms
occurred from the personal-level factors, which should not be
surprising because job satisfaction, job skills and capability
are consistently associated with depressive symptoms.
60–62
Furthermore, the lowest mean score among the components
Table 5 Contributions (Direct, Indirect and Total) of Socio-Ecological Framework (SEF) to Depressive Symptoms Among Rural
Health Workers
Variable/Component Direct Indirect Total
Coefcient SC
*
p
**
Coefcient SC pCoefcient SC p
Personal-level factors −2.32 −0.30 <0.001 0.062 0.008 0.111 −2.26 −0.29 <0.001
Interpersonal-
level factors
Interaction with
villagers
−0.47 −0.07 0.052 – – – −0.47 −0.07 0.052
Organizational-
level factors
Interaction with
colleagues/
monitoring and
evaluation
−0.078 −0.01 0.770 – – – −0.078 −0.01 0.770
Reinforcing factors 0.45 0.054 0.067 – – – 0.45 0.05 0.067
Work load and
roles interference
−0.74 −0.10 <0.001 −0.11 −0.015 0.071 −0.86 −0.11 <0.001
Community-
level factors
Culture and
environment of
village
−0.099 −0.01 0.767 −0.14 −0.017 0.079 −0.24 −0.03 0.434
Family/colleague
support
−1.28 −0.18 <0.001 −0.104 −0.015 0.077 −1.38 −0.20 <0.001
Belief in oneself as a religious man/
woman
3.43 0.13 <0.001 – – – 3.43 0.13 <0.001
Family history of mental illness −1.48 −0.09 <0.01 – – – −1.48 −0.09 <0.01
Age −0.019 −0.02 0.564 – – – −0.019 −0.02 0.564
Female gender 1.69 0.12 <0.001 −0.118 −0.008 0.090 1.57 0.11 <0.001
Saying daily prayer −0.043 −0.001 0.917 – – – −0.043 −0.001 0.917
Education level −0.56 −0.08 <0.01 – – – −0.56 −0.08 <0.01
Income −0.84 −0.12 <0.001 – – – −0.84 −0.12 <0.001
Not smoking −0.80 −0.02 0.190 – – – −0.80 −0.02 0.190
Having chronic illness −1.64 −0.09 <0.001 – – – −1.64 −0.09 <0.001
Note: **The statistically signicant contributions are shown in bold.
Abbreviation: *SC, standardized coefcient.
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was found among the reinforcing factors, which shows that
the RHWs’ organization has not worked well in providing
them with encouraging mechanisms and recreational and
welfare programs. This is unfortunate since such programs
are associated with improved efciency,
61
reduced medical
costs
63
and depressive symptoms
13
among the workers. One
of the major organizational deciencies with the job of an
RHW is the wide range of responsibilities and also the high
level of accountability to a wide range of health personnel
from various levels of the healthcare system,
12
which make
them more vulnerable to depressive symptoms. Others have
also pointed out that the lack of denition in RHWs’ roles
and duties is an important factor for depressive symptoms.
64
Similarly, the support of family/colleagues, from the
community component, provided both direct and total
contributions to depressive symptoms. These results are
in line with the well-known effect of social factors on
depression.
65
Different studies have indicated the role of
family and peer social support,
28
emotional support
66
and
provision of social networks
31
in preventing depressive
symptoms. RHWs essentially have a rural living, which
may expose them to circumstances, conditions and beha-
viors that challenge their health and may increase the
prevalence of depressive symptoms among them. For
instance, rural living is associated with severe depressive
symptoms and poor mental health,
64
possibly due to
resource disparities that are common in rural areas.
67
As
one example, the place of residence and having public
facilities, such as parks and transportation, have been
reported to affect the outbreak of depressive symptoms.
53
Similarly, work load and roles interference, as organiza-
tional factors, were equally contributory to depressive symp-
toms as some other components, but with no indirect
contribution. The subjects had been working as RHWs for an
average of 16 years, and depressive symptoms are known to be
associated with length of employment.
68
Thus, burnout may
mediate depressive symptoms among vulnerable subjects. For
instance, many RHWs may have additional duties such as child
rearing.
62
Furthermore, only a few percent (17%) of RHWs
reported being interested in their job as an RHW. The job
dissatisfaction may arise from a number of direct factors,
such as poor compensation, the overburden of duties, the effect
on other areas of life (e.g. family, social) and poor utilization of
skills
69–71
For instance, long daily commutes or overburden or
shift duties may directly disturb people’s eating and cooking
times; provide inadequate time for amusement, sexual fulll-
ment, affection and communication with their partner; reduce
their participation in family affairs; increase the pressure to
meet nancial expenses from nagging spousal or children’s
demands; or affect their resilience in general.
We found that the belief in oneself as a religious person was
the strongest inverse contributor to the risk of depressive
symptoms. Iman (i.e. faith/belief) is one of the ten fundamental
qualities that is expected among Muslims in order for them to
receive God’s mercy and help (Quran, verse 33:35) during
predestined denite testing, “And We will surely test you
with something of fear and hunger and a loss of wealth and
lives and fruits, but give good tidings to the patient” (verse
2:155). In addition, the protective effect of religious beliefs can
be easily understood through conventional theoretical models;
Figure 2 Standardized coefcients and relations of socio-ecological framework (SEF)-based factors with depressive symptoms among rural health workers in Iran.
Abbreviation: IF, indirect factors.
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for instance, the theory of hope:
72
“So, verily, with every
difculty, there is relief: Verily, with every difculty there is
relief.” (Quran, 94:5–6). The association of such theoretical
models with depression is well recognized, even among non-
believers.
73
In our study, the standardized direct contribution to depres-
sive symptoms due to female gender was about 1.7 (Table 5).
There was no indirect contribution of gender on depressive
symptoms (Table 5), which may mean that any possible gender
difference in the frequency (and the nature) of depressive
symptoms is possibly biological, and not due to social or
cultural factors.
74
One simple example from our sample was
that entering the menopause was signicantly associated with
depressive symptoms (Table 1). So, this supports our premise
that time-bound biological changes (e.g. in ovarian hormones
and hippocampal volume
64,67
) may be articially reected as
a higher risk or frequency of depressive symptoms among
females.
74
Another supporting argument could be related to
the presumed change in social attitude to promote supposed
equality in the West and the semi-West; yet, there has been no
clear change in terms of a reduction in the female:male depres-
sion ratio.
14
The WHO reports also support that there is no
difference in the prevalence of depressive symptoms between
males and females.
22
Moreover, besides the above reasons,
there are many other counter-explanations that must be over-
come before a female risk differential could be reliably
accepted. For instance, since females have two identical copies
of the X chromosome, they are likely to be better protected.
75
Lastly, we recruited our subjects from all rural areas of the
selected counties. Moreover, as far as we are aware, this was
the rst study to examine the simultaneous contribution of
multi-level factors for depressive symptoms among RHWs.
However, our study has similar limitations to other published
studies of comparable study design. The SEF can be fairly
extensive and so is the risk of depressive symptoms, and,
therefore, it was not feasible for us to cover many factorial
contexts (e.g. genetic, political, biological, psychoneurotic)
without overburdening our participants. As an example, the
target population in our study was RHWs, who unequivocally
were in direct contact with the recipients of their service. A
previous study has shown that emotional labor may also lead to
emotional burnout, which further increases the risk for depres-
sive symptoms.
76
Conclusion
Depressive symptoms were common among RHWs, aris-
ing from all personal-, interpersonal-, organizational- and
community-level factors, which reinforces that RHWs are
possibly more prone to depressive symptoms than other
medical workers, both in Iran and elsewhere. In addition,
our SEF had adequate internal consistency and factor
structure parameters to be applied in countries in the
MENA region, like Iran, as a theoretical framework to
plan for interventional efforts aiming at preventing depres-
sive symptoms among RHWs. Given these results, we are
condent in suggesting that the burden of depressive
symptoms can only be reduced through multi-factorial
interventions and rational perspectives. We believe that
our work could facilitate a better understanding of the
determinants that underlie depressive symptoms, and,
therefore, better interventional efforts to reduce the ever-
growing burden of depressive symptoms among the at-risk
subjects. RHWs are critical stakeholders in rural health-
care, so their health and welfare should be as high
a priority as the health and welfare of the general public.
Ethical Approval and Consent to
Participate
Ethical approval for the study was received from the
Ethics Committee in Research Affairs, Tabriz University
of Medical Sciences. All respondents were informed about
the aim of study and assured about the condentiality of
the data, and all signed a consent form.
Acknowledgments
We thank all the rural health workers who participated in
our study.
Author Contributions
All authors made a signicant contribution to the work
reported, whether that is in the conception, study design,
execution, acquisition of data, analysis and interpretation,
or in all these areas; took part in drafting, revising or
critically reviewing the article; gave nal approval of the
version to be published; have agreed on the journal to
which the article has been submitted; and agree to be
accountable for all aspects of the work.
Funding
No funds were received for this work.
Disclosure
The authors declare no potential competing interests for
this work.
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References
1. Lakhan R, Ekúndayò OT. Application of the ecological framework in
depression: an approach whose time has come. AP J Psychol Med.
2013;14:103–109.
2. WHO. Depression and Other Common Mental Disorders: Global
Health Estimates; 2017.
3. Letvak S, Ruhm CJ, McCoy T. Depression in hospital-employed
nurses. Clin Nurse Specialist. 2012;26:177–182.
4. Dehghan A, Ghavami L, Ghahramani F, Bazrafshan MR, Namavar S.
Prevalence of depression and its relation with their performance in
larestan rural health workers in 2010. J Rafsanjan Univ Med Sci.
2012;11:79–84.
5. Fushimi M. Prevalence of depressive symptoms and related factors in
Japanese employees: a comparative study between surveys from 2007
and 2010. Psychiatry J. 2015;2015:1–7. doi:10.1155/2015/537073
6. Alkhazrajy L, Sabah S, Abed SMH. Prevalence of depressive symp-
toms among primary health care providers in Baghdad. Int J Health
Psychol Res. 2014;2:1–20.
7. Ardekani Z, Kakooei H, Ayattollahi SM, Choobineh A, Seraji GN.
Prevalence of mental disorders among shift work hospital nurses in
Shiraz, Iran. Pak J Biol Sci. 2008;11(12):1605–1609. doi:10.3923/
pjbs.2008.1605.1609
8. Martin F, Poyen D, Bouderlique E, et al. Depression and burnout in
hospital health care professionals. Int J Occup Environ Health.
1997;3:204–209. doi:10.1179/oeh.1997.3.3.204
9. Obi IE, Aniebue PN, Okonkwo KO, Okeke TA, Ugwunna NC.
Prevalence of depression among health workers in Enugu, South
East Nigeria. Niger J Clin Pract. 2015;18:342–347. doi:10.4103/
1119-3077.151726
10. Kessler R, Barber C, Birnbaum HG, et al. Depression in the work-
place: effects on short-term disability. Health Aff. 1999;18:163–171.
doi:10.1377/hlthaff.18.5.163
11. Adler D, McLaughlin TJ, Rogers WH, Chang H, Lapitsky L, Lerner D.
Job performance decits due to depression. Am J Psychiatry. 2006;163
(9):1569–1576. doi:10.1176/ajp.2006.163.9.1569
12. Ansaripour S, Hasanzadeh A, Gramian N, Akhavan S, Moghadas T.
The prevalence of depression and its impact on health workers’
performance in Isfahan University of Medical Sciences, Iran, 2013.
J Isfahan Med School. 2016;33:1884–1890.
13. Costello CG. Depression: loss of reinforcers or loss of reinforcer
effectiveness? – republished article. Behav Ther. 2016;47
(5):595–599. doi:10.1016/j.beth.2016.08.007
14. Dehghani M, Zoladl M, Boland-Parvaz S, Keshtkaran Z,
Mahmoudi R, Jabbarnejad A. A survey on depression and its related
factors in nurses who work in Namazi Hospital of Shiraz University
of medical sciences-2008. Iran Occup Health. 2009;6:29–34.
15. Maharaj SLT, Lees S, Lal S. Prevalence and risk factors of depression,
anxiety, and stress in a cohort of Australian nurses. Int J Environ Res
Public Health. 2018;16(1):61. doi:10.3390/ijerph16010061
16. Shibata AOK, Harada K, Nakamura Y, Muraoka I. Psychological,
social, and environmental factors to meeting physical activity recom-
mendations among Japanese adults. Int J Behav Nutr Phys Act.
2009;6:1–12.
17. Ishii KSA, Shibata K, Oka K. Environmental, psychological, and
social inuences on physical activity among Japanese adults: struc-
tural equation modeling analysis. Int J Behav Nutr Phys Act. 2010;7
(1):61. doi:10.1186/1479-5868-7-61
18. Reupert A, Maybery D, Cox M, Scott Stokes E. Place of family in
recovery models for those with a mental illness. Int J Ment Health
Nurs. 2015;24:495–506. doi:10.1111/inm.12146
19. Robinson T. Applying the socio-ecological model to improving fruit and
vegetable intake among low-income African Americans. J Community
Health. 2008;33(6):395–406. doi:10.1007/s10900-008-9109-5
20. Sallis JF, Owen N, Fisher E. Ecological models of health behavior.
Health Behav. 2015;5:43–64.
21. Glanz K, Rimer BK, Viswanath K. Health Behavior and Health
Education: Theory, Research, and Practice. John Wiley & Sons;
2008.
22. Krug E, Mercy JA, Dahlberg LL, Zwi AB. The world report on
violence and health. Lancet. 2002;360(9339):1083–1088.
doi:10.1016/S0140-6736(02)11133-0
23. McLaren L, Hawe P. Ecological perspectives in health research.
J Epidemiol Community Health. 2005;59:6–14. doi:10.1136/
jech.2003.018044
24. Stokols D. Establishing and maintaining healthy environments:
toward a social ecology of health promotion. Am Psychol.
1992;47:6. doi:10.1037/0003-066X.47.1.6
25. McLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological per-
spective on health promotion programs. Health Educ Q.
1988;15:351–377. doi:10.1177/109019818801500401
26. Aghdam FBMM, Jafarabadi MA, Allahverdipour H,
Nikookheslat SD, Nourizadeh R. Explaining the role of personal,
social and physical environment factors on employed women’s phy-
sical activity: a structural equation analysis. Glob J Health Sci.
2013;5:189.
27. Bakhtari F, Nadrian H, Matlabi H, Sarbakhsh P, Bidar M. Personal,
interpersonal, and organizational predictors of the mode of delivery
among urban women: a prospective study with socio-ecological
approach. Clin Nurs Res. 2017;1054773817740530.
28. Sameroff AJPS, editors. Contrasting ecological models for adolescent mental
health, problem behavior, and academic achievement. biannual meetings of
the Society for Research in Adolescence; 1998; San Diego, CA.
29. Schueller SM, Aguilera A, Mohr DC. Ecological momentary inter-
ventions for depression and anxiety. Depress Anxiety.
2017;34:540–545. doi:10.1002/da.22649
30. Poleshuck E, Perez-Diaz W, Wittink M, et al. Resilience in the midst
of chaos: socioecological model applied to women with depressive
symptoms and socioeconomic disadvantage. J Community Psychol.
2019;47:1000–1013. doi:10.2/jcop.22188
31. Smokowski PR, Evans CB, Cotter KL, Guo S. Ecological correlates
of depression and self-esteem in rural youth. Child Psychiatry Hum
Dev. 2014;45:500–518. doi:10.1007/s10578-013-0420-8
32. Olson J, Goddard HW. An ecological risk/protective factor approach
to understanding depressive symptoms in adolescents. J Ext.
2010;48:1–10.
33. MacCallum RC, Browne MW, Sugawara HM. Power analysis and
determination of sample size for covariance structure modeling.
Psychol Methods. 1996;1:130. doi:10.1037/1082-989X.1.2.130
34. Beck AT, Beck RW. Screening depressed patients in family practice:
a rapid technic. Postgrad Med. 1972;52:81–85. doi:10.1080/
00325481.1972.11713319
35. Dadfar M, Kalibatseva Z. Psychometric properties of the persian
version of the short beck depression inventory with Iranian psychia-
tric outpatients. Scientica (Cairo). 2016;2016:8196463.
36. Costello C. Social factors associated with depression: a retrospective
community study. Psychol Med. 1982;12(2):329–339. doi:10.1017/
S0033291700046663
37. Ngin CPK, Tuot S, Chhoun P, Yi R, Yi S. Social and behavioural
factors associated with depressive symptoms among university stu-
dents in Cambodia: a cross-sectional study. BMJ Open. 2018;8:
e019918. doi:10.1136/bmjopen-2017-
38. Madsen IEH, Nyberg ST, Magnusson Hanson LL, et al.; IPD-Work
Consortium. Job strain as a risk factor for clinical depression: sys-
tematic review and meta-analysis with additional individual partici-
pant data. Psychol Med. 2017;47(8):1342–1356. doi:10.1017/
S003329171600355X.
39. Bernburg MVK, Vitzthum K, Groneberg DA, Mache S. Physicians‘
occupational stress, depressive symptoms and work ability in relation to
their working environment: a cross-sectional study of differences among
medical residents with various specialties working in German hospitals.
BMJ Open. 2016;6(6):e011369. doi:10.1136/bmjopen-2016-011369
Dovepress Bakhtari et al
Journal of Multidisciplinary Healthcare 2020:13 submit your manuscript | www.dovepress.com
DovePress
979
40. Theorell THA, Hammarström A, Aronsson G, et al. A systematic
review including meta-analysis of work environment and depressive
symptoms. BMC Public Health. 2015;15(1):738. doi:10.1186/
s12889-015-1954-4
41. Lawshe CH. A quantitative approach to content validity. Pers Psychol.
1975;28(4):563–575. doi:10.1111/j.1744-6570.1975.tb01393.x
42. Kline R. Principles and Practice of Structural Equation Modeling.
Vol. 2. New York: Guilford Press; 2004.
43. Liu J, Zhu B, Wu J, Mao Y. Job satisfaction, work stress, and turn-
over intentions among rural health workers: a cross-sectional study in
11 western provinces of China. BMC Fam Pract. 2019;20(1):9.
doi:10.1186/s12875-019-0904-0
44. Chowdhury Z, Javadi D. Iran’s community health worker program;
2014 [cited March 17, 2020]. Available from: http://www.chwcentral.
org/blog/iran’s-community-health-worker-program. Accessed August
25, 2020.
45. Indeed. Healthcare assistant salaries in the United Arab emirates;
2020 [cited March 17, 2020]. Available from: https://www.indeed.
ae/salaries/healthcare-assistant-Salaries. Accessed August 25, 2020.
46. Nabipour A, Gholami H, Amini A, Riahi SM, Ghanbarifar S,
Zirak MH. Prevalence of depression and its related factors in
Pishva District health network employees in 2013. 2015.
47. Fallah RFS, Amini K, Mohajeri M. Prevalence of depression in
personnel of Zanjan University of medical sciences. ZUMS J.
2011;19:107–113.
48. Vasconcelos EMD, Martino MMFD, França SPDS. Burnout and depres-
sive symptoms in intensive care nurses: relationship analysis. Rev Bras
Enferm. 2018;71(1):135–141. doi:10.1590/0034-7167-2016-0019
49. Strömberg R, Backlund LG, Löfvander M. A comparison between
the Beck’s Depression Inventory and the Gotland Male Depression
Scale in detecting depression among men visiting a drop-in clinic in
primary care. Nord J Psychiatry. 2010;64(4):258–264. doi:10.3109/
08039480903511407
50. Nezam S, Golwara AK, Jha PC, Khan SA, Singh S, Tanwar AS.
Comparison of prevalence of depression among medical, dental, and
engineering students in Patna using beck’s depression inventory II:
a cross-sectional study. J Family Med Prim Care. 2020;9(6):3005.
doi:10.4103/jfmpc.jfmpc_294_20
51. Titov N, Dear BF, McMillan D, Anderson T, Zou J, Sunderland M.
Psychometric comparison of the PHQ-9 and BDI-II for measuring
response during treatment of depression. Cogn Behav Ther. 2011;40
(2):126–136. doi:10.1080/16506073.2010.550059
52. World Bank. Exploring Factors Driving the Performance of Rural
Health Care in Papua New Guinea (English). Washington, DC:
World Bank; 2018. Available from: http://documents.worldbank.org/
curated/en/611831528823049450/Exploring-factors-driving-the-
performance-of-rural-health-care-in-Papua-New-Guinea. Accessed
August 25, 2020.
53. Weich SBM, Prince M, Burton E, Erens B, Sproston K. Mental health
and the built environment: cross-sectional survey of individual and
contextual risk factors for depression. Br J Psychiatry.
2002;180:428–433. doi:10.1192/bjp.180.5.428
54. Vakili MEFS, Hossein SMH, Dehghani Tafti MH. Prevalence of
depression and its related factors among truck drivers in Yazd
Province −2008. Iran Occup Health J. 2010;6:69–76.
55. Khumalo T, Plattner I. The relationship between locus of control and
depression: a cross-sectional survey with university students in
Botswana. S Afr J Psychiatr. 2019;25:1221. doi:10.4102/sajpsychia-
try.v25i0.1221
56. Xu W, Pan Z, Li Z, Lu S, Zhang L. Job burnout among primary
healthcare workers in rural China: a multilevel analysis. Int J Environ
Res Public Health. 2020;17(3):727. doi:10.3390/ijerph17030727
57. Gómez-Urquiza JLD-SE, Albendín-García L, Vargas-Pecino C,
Ortega-Campos EM, Cañadas-de la Fuente GA. Prevalence of burn-
out syndrome in emergency nurses: a meta-analysis. Crit Care Nurse.
2017;37:e1–e9. doi:10.4037/ccn2017508
58. Soler JKYH, Esteva M, Dobbs F, et al. Burnout in European family
doctors: the EGPRN study. Fam Pract. 2008;25:245–265.
doi:10.1093/fampra/cmn038
59. Blanchard PTD, Albiges-Sauvin L, Dewas S, et al. Prevalence and
causes of burnout amongst oncology residents: a comprehensive
nationwide cross-sectional study. Eur J Cancer. 2010;46:2708.
doi:10.1016/j.ejca.2010.05.014
60. Faragher EB, Cass M, Cooper CL. The relationship between job
satisfaction and health: a meta-analysis. Occup Environ Med.
2005;62:105–112. doi:10.1136/oem.2002.006734
61. Howarth A, Quesada J, Mills PR. A global, cross cultural study
examining the relationship between employee health risk status and
work performance metrics. Ann Occup Environ Med. 2017;29:17.
doi:10.1186/s40557-017-0172-1
62. O’Donnell LA, Deldin PJ, Grogan-Kaylor A, et al. Depression and
executive functioning decits predict poor occupational functioning
in a large longitudinal sample with bipolar disorder. J Affect Disord.
2017;215:135–142. doi:10.1016/j.jad.2017.03.015
63. Aldana SG, Merrill RM, Price K, Hardy A, Hager R. Financial
impact of a comprehensive multisite workplace health promotion
program. Prev Med. 2005;40:131–137. doi:10.1016/j.
ypmed.2004.05.008
64. Kessler RC, Bromet EJ. The epidemiology of depression across
cultures. Annu Rev Public Health. 2013;34:119–138. doi:10.1146/
annurev-publhealth-031912-114409
65. Grav S, Hellzèn O, Romild U, Stordal E. Association between social
support and depression in the general population: the HUNT study,
a cross-sectional survey. J Clin Nurs. 2012;21:111–120. doi:10.1111/
j.1365-2702.2011.03868.x
66. Santini ZI, Koyanagi A, Tyrovolas S, Mason C, Haro JM. The
association between social relationships and depression:
a systematic review. J Affect Disord. 2015;175:53–65. doi:10.1016/
j.jad.2014.12.049
67. Sakuraya A, Imamura K, Inoue A, et al. Workplace social capital and
the onset of major depressive episode among workers in Japan: a
3-year prospective cohort study. J Epidemiol Community Health.
2017;71:606–612. doi:10.1136/jech-2016-208561
68. DeSanto IJ, Cullen M, Cantley L, Slade M, Fiellin M, Kasl S. Effects
of externally rated job demand and control on depression diagnosis
claims in an industrial cohort. Am J Epidemiol. 2010;171:303–311.
doi:10.1093/aje/kwp359
69. Jayasuriya R, Whittaker M, Halim G, Matineau T. Rural health
workers and their work environment: the role of inter-personal factors
on job satisfaction of nurses in rural Papua New Guinea. BMC Health
Serv Res. 2012;12(1):156. doi:10.1186/1472-6963-12-156
70. Jin Y, Wang H, Wang D, Yuan B. Job satisfaction of the primary healthcare
providers with expanded roles in the context of health service integration
in rural China: a cross-sectional mixed methods study. Hum Resour
Health. 2019;17(1):70. doi:10.1186/s12960-019-0403-3
71. Kebriaei A, Moteghedi M. Job satisfaction among community
health workers in Zahedan District, Islamic Republic of Iran.
East Mediterr Health J. 2009;15(5):1156–1163. doi:10.26719/
2009.15.5.1156
72. Leite A, de Medeiros A, Rolim C, et al. Hope theory and its relation
to depression: a systematic review. Ann Psychiatr Clin Neurosci.
2019;2:1014.
73. The Guardian. Non-believers turn to prayer in a crisis, poll nds;
2020 [cited March 18, 2020]. Available from: https://www.theguar
dian.com/world/2018/jan/14/half-of-non-believers-pray-says-poll.
Accessed August 25, 2020.
74. Albert P. Why is depression more prevalent in women? J Psychiatry
Neurosci. 2015;40(4):219–221. doi:10.1503/jpn.150205
75. Dunford A, Weinstock D, Savova V, et al. Tumor-suppressor genes
that escape from X-inactivation contribute to cancer sex bias. Nat
Genet. 2017;49:10–16. doi:10.38/ng.3726
Bakhtari et al Dovepress
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980
76. Jeung D, Kim C, Chang S. Emotional labor and burnout: a review of
the literature. Yonsei Med J. 2018;59(2):187–193. doi:10.3349/
ymj.2018.59.2.187
77. Bronfenbrenner U. Toward an experimental ecology of human
development. Am Psychol. 1977;32(7):513. doi:10.1037/0003-
066X.32.7.513
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