Health literacy of university employees: The role of sociodemographic characteristics and nutritional status

Abstract

BACKGROUND:

Health literacy plays a key role in promoting overall health and preventing diseases among individuals and communities. However, the literature regarding health literacy among university employees is still evolving and not thoroughly understood.

OBJECTIVE:

This study was conducted to examine the association between health literacy and sociodemographic characteristics and nutritional status of university employees in Jordan.

METHODS:

This study was conducted using a cross-sectional design. A total of 163 university employees participated by completing a demographics questionnaire and the Health Literacy Questionnaire. The Health Literacy Questionnaire is considered a comprehensive tool to assess health literacy, and it encompasses nine distinct scales. The body mass index was calculated by obtaining the participants’ height and weight.

RESULTS:

The results of multivariate analysis of variance showed that three factors had a statistically significant effect on the linear composite of the Health Literacy Questionnaire scales. These factors were the university employees’ age, highest level of education, and body mass index. Follow-up analyses revealed that university employees’ sociodemographic characteristics and nutritional status affect different domains of health literacy. Compared to overweight employees, those with normal body mass index had higher mean average scores on six (out of nine) scales of the Health Literacy Questionnaire.

CONCLUSION:

These results highlight the need for addressing the nutritional status and sociodemographic characteristics as a source of disparity in university employees’ health literacy. Such factors should be addressed in designing tailored health promotion interventions for university employees.

Keywords

Health literacy university employees health promotion nutritional status

1 Introduction

University campuses are recognized as distinct communities encompassing people with different sociodemographic characteristics [1]. University employees experience different health risks with evidence showing that the majority (65–79%) of employees’ time spent during work is sedentary [2]. Sedentary time, in turn, contributes to non-work physical activity [2, 3]; increasing the risk for many health problems such as low back pain, ischemic heart disease, diabetes mellitus, and psychological disturbances [4 –6]. Furthermore, university employees encounter certain psychosocial and organizational work factors that can negatively impact their health and well-being [7, 8]. Consequently, the literature shows that university employees’ level of stress is higher compared to employees of other occupations [9 –11]. Such heightened stress levels can lead to psychological strain and physical symptoms including headaches, tiredness, muscle and back pain [12].

This presentation shows that university employees, both academics and non-academics, are at higher risk for many detrimental, yet preventable, health problems. Meanwhile, health literacy plays a key role in promoting the overall health and preventing diseases among individuals and communities [13 –15]. Health literacy is considered a modifiable risk factor of health disparities and a significant predictor of health outcomes [16, 17]. The importance of health literacy lies mainly in enabling individuals and groups to make informed decisions regarding their health and the health of others [13 –15]. Making health-related decisions is not confined to ill people, but it is a process that applies to all aspects of life across different settings including work setting [18]. In addition, making health-related decisions applies to different age groups across the lifespan [19, 20]. Being health literate at an earlier stage of life, like childhood and early adulthood, and/or at the workplace could help individuals making informed decisions both at work and outside of work [21].

However, the literature regarding health literacy among university employees is still evolving and not thoroughly understood. A key finding of many research papers has been the limited health literacy levels even among the educated university employees [22 –24]. The literature regarding the association between university employees’ sociodemographic variables and health literacy provides mixed results [22 –25]. Such variations in the results could be attributed to variations in the methodologies of the conducted studies and utilization of less comprehensive tools to measure health literacy [26]. Health literacy measurement varied across studies conducted in university employees, and some researchers relied on tools that assess merely reading and numeracy skills (e.g., the Newest Vital Sign [NVS]). Also, the variations in the results highlight the importance of measuring health literacy using comprehensive, well-established tools that capture this multidimensional construct [26 –28].

Thus, this study was conducted to examine the association between health literacy and sociodemographic characteristics and nutritional status of university employees in Jordan. This study is important because it is the first study to investigate the level of health literacy among university employees in Jordan. Moreover, it is one of the first studies conducted around the world using a comprehensive, valid tool to assess health literacy among university employees. Thorough measurement of health literacy facilitates better understanding of how the sociodemographic variables of university employees affect its different dimensions.

2 Methods

2.1 Design and setting

This study was conducted using a cross-sectional design in a sample of university employees in Jordan.

2.2 Sampling and participants

The participants were recruited from Jordan University of Science and Technology, including academic and non-academic employees. Purposive sampling was used to recruit the participants. Two inclusion criteria were applied: a) being a full-time employee, and b) having at least one year of experience.

G*Power analysis was used to estimate the current study’s required sample size and revealed that approximately 140 participants should be recruited. The following parameters were used:α= 0.05, power = 0.8, and small-medium effect size [29]. Two hundred and fifty university employees were invited to participate in this study. A total of 163 employees responded (response rate = 65%) by signing the informed consent and the paper-based questionnaires.

2.3 Data collection tools

Data were collected using a demographics questionnaire and the HLQ (Arabic version). The authors created the demographics questionnaire to conduct the current study. The sociodemographic variables included in the current study were employees’ age, gender, employment status (academic vs. non-academic), the highest level of education, smoking (smoker vs. non-smoker), and regular exercise. The authors used self-report to collect data about these sociodemographic variables. Regarding the smoking status, the participants were asked to self-report whether they are current smokers or not. Regarding the regular exercise, the participants were asked to self-report whether they practice any moderate-intensity physical activity for at least 150 minutes per week. The nutritional status was assessed by obtaining the employees’ body mass index (BMI) which is considered a widely accepted and inexpensive anthropometric indicator of nutritional status among healthy adults [30 –32]. A trained research assistant obtained the heights and weights of the participants. The values of the heights and weights were used to calculate the BMI for each participant, which was then used as an indicator of the university employees’ nutritional status. The BMI was calculated using the formula: weight (kg) ÷ [height (m)]². The following categorization was used to classify the participants [30]: a) underweight (BMI <18.5), b) healthy weight (BMI = 18.5–24.9), c) overweight (BMI = 25–29.9), and d) obesity (BMI ≥30).

The HLQ was used to collect data regarding the health literacy of university employees. The translated version was provided by the authors of the original HLQ, and the translation process was conducted following robust process to ensure consistency with the original version. The HLQ is a well-established, comprehensive tool that has been validated in different languages including Arabic [33]. The HLQ is a 44-item tool categorized into nine scales [34]. The tool is divided into two main parts: Part 1 (scales 1–5), and Part 2 (scales 6–9). The response categories for Part 1 range from “strongly disagree” (1) to “strongly agree” (4). The categories for Part 2 are “cannot do or always difficult” (1), “usually difficult” (2), “sometimes difficult” (3), “usually easy” (4), and “always easy” (5). The total score for each of the nine scales is calculated by calculating the mean average for the items within each scale. Possible total scores range from 1–4 and 1–5 for Part 1 and Part 2 scales, respectively. Regarding the validity and reliability of the HLQ, the tool was developed using a rigorous, validity-driven approach [34]. The internal consistency of the nine scales of the Arabic HLQ was assessed, and the results showed acceptable values of Cronbach’sα (ranging from 0.75 to 0.85).

2.3.1 Measures

For data analysis purposes, the independent variables were categorized to perform the statistical analyses. Participants’ age was categorized as: (1) 21–30 years, (2) 31–40 years, (3) 41–50 years, and (4) 51–60 years. Regarding the participants education, the following categorization was applied: (1) ≤High school, (2) 2-year college, (3) Bachelor’s Degree, (4) Master’s Degree, and (5) Doctoral Degree. The BMI categories were as follows: (1) Normal, (2) Overweight, and (3) Obese.

2.4 Data analysis

Data were analyzed using SPSS (version 23). Descriptive statistics and frequencies, bivariate correlations, Multivariate analysis of variance (MANOVA), and follow-up analyses were all performed. The scores of the nine HLQ scales were considered the dependent variables, whereas the independent variables were the sociodemographic variables and nutritional status (i.e., age, gender, employment status (academic vs. non-academic), the highest level of education, smoking, regular exercise, and BMI). MANOVA was first conducted to examine the effect of the independent variables on the linear combination of the dependent variables. Roy’s Largest Root test was used to interpret the results considering that: a) some of the independent variables had more than two groups, and b) the sample sizes of the groups were not equal [35]. Then, follow-up analyses were performed using one-way ANOVA’s and independentt-tests while applying Bonferroni adjustment to avoid inflation of Type I error. These follow-up analyses were performed to detect the exact source of the statistically significant mean differences in the HLQ scales based on the independent variables. The one-way ANOVA was conducted to detect the differences for the independent variables age, education, and BMI. On the other hand, the independentt-tests were performed to detect the differences for the independent variables gender, employment status, smoking, and regular exercises. TheP-values and 95% confidence intervals (95% CI) were used to determine the statistical significance of the mean differences.

2.5 Ethical considerations

This study was approved by the Institutional Review Board (IRB) committee at Jordan University of Science and Technology. Informed consent was obtained from each participant before asking the employees to complete the data collection questionnaires.

3 Results

3.1 Participants’ characteristics

The participants’ average age was 39.9 years old (SD = 8.69), and male participants represented 57.7% of the study sample. Most of the participants had a non-academic employment status. Regarding the BMI, the mean average of the values was 27.3 kg/m² (SD = 3.92). Regarding the categorization of the BMI, none of the participants had an underweight status (BMI range = 18.7 to 40.68 kg/m²). Table 1 summarizes the participants’ characteristics and the mean average of the HLQ scales.

Table 1
Participants’ demographic characteristics

Variable n Percentage

Age category:

21–30 years 26 16

31–40 years 64 39.3

41–50 years 52 31.9

51–60 years 21 12.9

Gender:

Male 94 57.7

Female 69 42.3

Employment status:

Academic 34 20.9

Non-academic 129 79.1

Highest level of education:

≤High school 29 17.8

2-year college 38 23.3

Bachelor’s Degree 61 37.4

Master’s Degree 19 11.7

Doctoral Degree 16 9.8

Smoking:

Yes 59 63.8

No 104 36.2

Regular exercise:

Yes 23 14.1

No 140 85.9

BMI category:

Normal (18.5–24.9 kg/m²) 42 25.8

Overweight (25–29.9 kg/m²) 83 50.9

Obese (≥30 kg/m²) 38 23.3

HLQ scales

Scale Mean SD

Feeling understood and supported by healthcare providers 2.60 0.66

Having sufficient information to manage my health 2.81 0.58

Actively managing my health 2.78 0.57

Social support for health 2.85 0.57

Appraisal of health information 2.90 0.52

Ability to actively engage with healthcare providers 3.49 0.81

Navigating the healthcare system 3.54 0.79

Ability to find good health information 3.63 0.75

Understand health information 3.70 0.74

Variable	n	Percentage
Age category:
21–30 years	26	16
31–40 years	64	39.3
41–50 years	52	31.9
51–60 years	21	12.9
Gender:
Male	94	57.7
Female	69	42.3
Employment status:
Academic	34	20.9
Non-academic	129	79.1
Highest level of education:
≤High school	29	17.8
2-year college	38	23.3
Bachelor’s Degree	61	37.4
Master’s Degree	19	11.7
Doctoral Degree	16	9.8
Smoking:
Yes	59	63.8
No	104	36.2
Regular exercise:
Yes	23	14.1
No	140	85.9
BMI category:
Normal (18.5–24.9 kg/m²)	42	25.8
Overweight (25–29.9 kg/m²)	83	50.9
Obese (≥30 kg/m²)	38	23.3
HLQ scales
Scale	Mean	SD
Feeling understood and supported by healthcare providers	2.60	0.66
Having sufficient information to manage my health	2.81	0.58
Actively managing my health	2.78	0.57
Social support for health	2.85	0.57
Appraisal of health information	2.90	0.52
Ability to actively engage with healthcare providers	3.49	0.81
Navigating the healthcare system	3.54	0.79
Ability to find good health information	3.63	0.75
Understand health information	3.70	0.74

3.2 Bivariate correlations between the dependent variables

The bivariate correlations between the HLQ scales were examined using Pearson’sr correlation. This step was performed based on the evidence that a multivariate effect exists when the bivariate correlations between the dependent variables range from 0.30 to 0.90. The results showed that the HLQ scales were all positively correlated (r range = 0.33 to0.86).

3.3 Multivariate analysis

The results of the MANOVA revealed that three independent variables had a statistically significant effect on the linear composite of the dependent variables (the HLQ scales). These independent variables were as follow: a) Age: Roy’s Largest Root = 0.18, F (9, 143) = 2.87,p = 0.004; b) education: Roy’s Largest Root = 0.18, F (9, 144) = 2.90,p = 0.004; and c) BMI: Roy’s Largest Root = 0.24, F (9, 142) = 2.90,p < 0.001. The effects of the remaining four independent variables (gender, employment status, smoking, and regular exercises) were not statistically significant (Table 2). Regarding the interaction (between the independent variables) effects, the results showed that the following interactions were statistically significant: age X BMI, age X education, age X employment status, age X regular exercise, education X BMI, education X regular exercise, and regular exercise X smoking. The interaction of all independent variables was statistically significant; Roy’s Largest Root = 3.98, F (101, 61) = 2.40,p < 0.001 (Table 2).

Table 2
Multivariate analysis

Independent variable Roy’s Largest Root F (df’s) P-Value Partial eta squared

Age 0.18 2.87 (9, 143) 0.004 0.15

Gender 0.04 0.64 (9, 141) 0.77 0.04

Employment status 0.07 1.08 (9, 141) 0.38 0.07

Education 0.18 2.90 (9, 144) 0.004 0.15

Smoking 0.03 0.43 (9, 141) 0.92 0.03

Regular exercises 0.12 1.83 (9, 141) 0.07 0.11

BMI 0.24 3.70 (9, 142) <0.001 0.19

Significant interactions

Age X BMI 0.34 3.21 (9, 84) 0.002 0.26

Age X Education 0.48 4.64 (9, 87) <0.001 0.32

Age X Employment status 0.37 3.29 (9, 80) 0.002 0.27

Age X Regular exercise 0.40 3.62 (9, 81) 0.001 0.29

Education X BMI 0.30 2.85 (9, 86) 0.006 0.23

Education X Regular exercise 0.38 3.44 (9, 82) 0.001 0.27

Regular exercise X Smoking 0.24 2.08 (9, 79) 0.041 0.19

All variables 3.98 2.4 (101, 61) <0.001 0.80

Independent variable	Roy’s Largest Root	F (df’s)	P-Value	Partial eta squared
Age	0.18	2.87 (9, 143)	0.004	0.15
Gender	0.04	0.64 (9, 141)	0.77	0.04
Employment status	0.07	1.08 (9, 141)	0.38	0.07
Education	0.18	2.90 (9, 144)	0.004	0.15
Smoking	0.03	0.43 (9, 141)	0.92	0.03
Regular exercises	0.12	1.83 (9, 141)	0.07	0.11
BMI	0.24	3.70 (9, 142)	<0.001	0.19
Significant interactions
Age X BMI	0.34	3.21 (9, 84)	0.002	0.26
Age X Education	0.48	4.64 (9, 87)	<0.001	0.32
Age X Employment status	0.37	3.29 (9, 80)	0.002	0.27
Age X Regular exercise	0.40	3.62 (9, 81)	0.001	0.29
Education X BMI	0.30	2.85 (9, 86)	0.006	0.23
Education X Regular exercise	0.38	3.44 (9, 82)	0.001	0.27
Regular exercise X Smoking	0.24	2.08 (9, 79)	0.041	0.19
All variables	3.98	2.4 (101, 61)	<0.001	0.80

3.4 Follow-up analyses

The follow-up analysis (between-subjects effects) showed that only the BMI independent variable had statistically significant results. The BMI effect was significant on six HLQ scales: “Feeling understood and supported by healthcare providers,” “Actively managing my health,” “Appraisal of health information,” “Ability to actively engage with healthcare providers,” “Navigating the healthcare system,” and “Ability to find good health information” (Table 3). All other between-subjects effects of the rest of the independent variables were not statistically significant.

Table 3
Univariate analysis (between-subjects effects)

Independent variable Dependent variable F (2, 149) P-value Partial eta squared

BMI HPS 8.48 <0.001 0.10

HSI 1.87 0.16 0.02

AMH 7.74 0.001 0.09

SS 2.05 0.13 0.03

CA 8.03 <0.001 0.10

AE 3.90 0.023 0.05

NHS 3.81 0.024 0.05

FHI 5.01 0.008 0.06

UH 2.27 0.11 0.03

Independent variable	Dependent variable	F (2, 149)	P-value	Partial eta squared
BMI	HPS	8.48	<0.001	0.10
	HSI	1.87	0.16	0.02
	AMH	7.74	0.001	0.09
	SS	2.05	0.13	0.03
	CA	8.03	<0.001	0.10
	AE	3.90	0.023	0.05
	NHS	3.81	0.024	0.05
	FHI	5.01	0.008	0.06
	UH	2.27	0.11	0.03

Note. HPS: Feeling understood and supported by healthcare providers, HSI: Having sufficient information to manage my health, AMH: Actively managing my health, SS: Social support for health, CA: Appraisal of health information, AE: Ability to actively engage with healthcare providers, NHS: Navigating the healthcare system, FHI: Ability to find good health information, UHI: Understand health information.

The results of the follow-up analyses are presented in Tables 4 and 5. In Table 4, comparisons of the differences in the means are presented. Age categories presented are: 21–30 years (1), 31–40 years (2), 41–50 years (3), and 51–60 years (4). The education categories are: ≤High school (1), 2-year college (2), Bachelor’s Degree (3), Master’s Degree (4), and Doctoral Degree (5). The BMI categories are: Normal (1), Overweight (2), and Obese (3). The presented mean differences show that BMI had a statistically significant effect on six HLQ scales; participants with a normal BMI had a higher mean average on these scales than overweight participants. Regarding education level, the results showed that participants with a doctoral degree had a statistically significant mean difference compared to those with high school or less education.

Table 4

Multiple comparisons based on one-way ANOVA analyses

IV category		HPS	HSI	AMH	SS	CA	AE	NHS	FHI	UHI
A	B	A-B	A-B	A-B	A-B	A-B	A-B	A-B	A-B	A-B
		(95% CI)	(95% CI)	(95% CI)	(95% CI)	(95% CI)	(95% CI)	(95% CI)	(95% CI)	(95% CI)
Age: 1	2	–0.13	–0.11	–0.08	0.12	–0.01	–0.05	0.08	0.21	0.09
		(–0.53, 0.27)	(–0.46, 0.24)	(–0.43, 0.26)	(–0.22, 0.46)	(–0.32, 0.31)	(–0.54, 0.45)	(–0.40, 0.57)	(–0.24, 0.66)	(–0.36, 0.54)
	3	–0.03	–0.02	0.06	0.35	0.11	–0.04	0.10	0.03	0.02
		(–0.45, 0.38)	(–0.39, 0.35)	(–0.30, 0.42)	(–0.01, 0.71)	(–0.22, 0.44)	(–0.56, 0.47)	(–0.41, 0.60)	(–0.44, 0.51)	(–0.45, 0.48)
	4	–0.25	–0.24	–0.23	0.08	–0.11	0.17	0.14	0.35	0.24
		(–0.77, 0.26)	(–0.69, 0.22)	(–0.68, 0.21)	(–0.36, 0.52)	(–0.51, 0.30)	(–0.46, 0.81)	(–0.49, 0.76)	(–0.23, 0.94)	(–0.34, 0.82)
Age: 2	3	0.10	0.09	0.14	0.23	0.11	0.01	0.01	–0.18	–0.07
		(–0.23, 0.43)	(–0.2, 0.38)	(–0.14, 0.42)	(–0.05, 0.51)	(–0.15, 0.38)	(–0.40, 0.41)	(–0.38, 0.41)	(–0.55, 0.20)	(–0.44, 0.30)
	4	–0.12	–0.13	–0.15	–0.04	–0.10	0.22	0.06	0.14	0.15
		(–0.55, 0.30)	(–0.50, 0.25)	(–0.52, 0.22)	(–0.41, 0.32)	(–0.44, 0.24)	(–0.31, 0.75)	(–0.46, 0.57)	(–0.34, 0.63)	(–0.33, 0.63)
Age: 3	4	–0.22	–0.22	–0.29	–0.27	–0.21	0.22	0.04	0.32	0.22
		(–0.66, 0.22)	(–0.61, 0.17)	(–0.67, 0.09)	(–0.65, 0.11)	(–0.57, 0.14)	(–0.34, 0.76)	(–0.50, 0.58)	(–0.18, 0.82)	(–0.28, 0.72)
Education: 1	2	–0.15	–0.22	–0.06	–0.02	–0.01	–0.22	–0.22	–0.23	–0.28
		(–0.61, 0.32)	(–0.62, 0.18)	(–0.46, 0.34)	(–0.42, 0.38)	(–0.38, 0.36)	(–0.79, 0.34)	(–0.77, 0.33)	(–0.73, 0.28)	(–0.78, 0.23)
	3	–0.15	–0.11	0.12	–0.10	–0.01	–0.12	–0.19	–0.30	–0.20
		(–0.57, 0.26)	(–0.47, 0.25)	(–0.24, 0.48)	(–0.46, 0.27)	(–0.34, 0.33)	(–0.63, 0.39)	(–0.68, 0.30)	(–0.76, 0.15)	(–.66,.25)
	4	–0.27	–0.27	–0.06	–0.19	–0.19	–0.33	–0.30	–0.60	–0.43
		(–0.82, 0.28)	(–0.75, 0.21)	(–0.54, 0.41)	(–0.67, 0.29)	(–0.63, 0.25)	(–1.0, 0.34)	(–0.95, 0.35)	(–1.2, 0.01)	(–1.0, 0.17)
	5	–0.24	–0.49	0.12	–0.25	–0.04	–0.50	–0.71^**	–0.74^**	–0.59
		(–0.82, 0.33)	(–0.99, 0.01)	(–0.38, 0.62)	(–0.75, 0.26)	(–0.50, 0.43)	(–1.2, 0.21)	(–1.4, 0.03)	(–1.4, –0.12)	(–1.2, 0.04)
Education: 2	3	–0.01	0.11	0.18	–0.08	0.01	0.10	0.03	–0.08	0.07
		(–0.39, 0.38)	(–0.23, 0.44)	(–0.16, 0.51)	(–0.41, 0.26)	(–0.30, 0.31)	(–0.37, 0.58)	(–0.43, 0.49)	(–0.50, 0.35)	(–0.35, 0.50)
	4	–0.12	–0.05	–0.01	–0.17	–0.18	–0.11	–0.08	–0.38	–0.16
		(–0.64, 0.40)	(–0.50, 0.40)	(–0.46, 0.44)	(–0.63, 0.28)	(–0.60, 0.23)	(–0.75, 0.52)	(–0.70, 0.53)	(–0.95, 0.20)	(–0.73, 0.41)
	5	–0.10	–0.27	0.18	–0.23	–0.03	–0.28	–0.49	–0.52	–0.32
		(–0.64, 0.45)	(–0.74, 0.20)	(–0.30, 0.65)	(–0.70, 0.25)	(–0.47, 0.41)	(–0.95, 0.39)	(–1.1, 0.16)	(–1.1, 0.08)	(–0.92, 0.28)
Education: 3	4	–0.11	–0.16	–0.19	–0.10	–0.18	–0.22	–0.11	–0.30	–0.23
		(–0.59, 0.36)	(–0.57, 0.25)	(–0.60, 0.23)	(–0.51, 0.32)	(–0.56, 0.19)	(–0.80, 0.36)	(–0.67, 0.45)	(–0.82, 0.23)	(–0.75, 0.29)
	5	–0.09	–0.37	–0.01	–0.15	–0.03	–0.38	–0.52	–0.44	–0.39
		(–0.59, 0.41)	(–0.81, 0.06)	(–0.44, 0.43)	(–0.59, 0.29)	(–0.43, 0.37)	(–0.90, 0.23)	(–1.1, 0.07)	(–0.99, 0.11)	(–0.94, 0.16)
Education: 4	5	0.02	–0.22	0.18	–0.05	0.15	–0.16	–0.41	–0.14	–0.16
		(–0.61, 0.66)	(–0.77, 0.33)	(–0.37, 0.74)	(–0.61, 0.50)	(–0.36, 0.66)	(–0.94, 0.62)	(–1.2, 0.34)	(–0.84, 0.56)	(–0.86, 0.54)
BMI: 1	2	0.38^**	0.17	0.36^**	0.20	0.31^**	0.42^*	0.41^*	0.42^**	0.30
		(0.10, 0.66)	(–0.09, 0.43)	(0.11, 0.61)	(–0.06, 0.46)	(0.08, 0.54)	(0.06, 0.78)	(0.06, 0.76)	(0.09, 0.75)	(–0.03, 0.63)
	3	–0.03	0.02	0.04	–0.01	–0.02	0.19	0.18	0.18	0.18
		(–0.37, 0.31)	(–0.30, 0.33)	(–0.26, 0.33)	(–0.32, 0.30)	(–0.29, 0.54)	(–0.24, 0.62)	(–0.25, 0.60)	(–0.22, 0.58)	(–0.22, 0.57)
BMI: 2	3	–0.41^**	–0.15	–0.32^**	–0.21	–0.33^**	–0.23	–0.23	–0.24	–0.12
		(–0.71, –0.12)	(–0.42, 0.12)	(–0.58, –0.07)	(–0.47, 0.06)	(–0.57, –0.10)	(–0.60, 0.14)	(–0.60, 0.12)	(–0.58, 0.10)	(–0.46, 0.22)

Note.^*p value <0.05;^**p value <0.01. Age categories: 21–30 years (1), 31–40 years (2), 41–50 years (3), and 51–60 years (4). Education categories: ≤High school (1), 2-year college (2), Bachelor’s Degree (3), Master’s Degree (4), and Doctoral Degree (5). BMI categories: Normal (1), Overweight (2), and Obese (3).

Table 5

Follow-up analyses (independentt-tests)

	HPS	HSI	AMH	SS	CA	AE	NHS	FHI	UHI
	M (SD)	M (SD)	M (SD)	M (SD)	M (SD)	M (SD)	M (SD)	M (SD)	M (SD)
Gender
Male	2.55 (0.61)	2.73^* (0.55)	2.74 (0.58)	2.78 (0.56)	2.85 (0.49)	3.45 (0.77)	3.37 (0.76)	3.55 (0.75)	3.65 (0.73)
Female	2.67 (0.71)	2.92^* (0.61)	2.85 (0.56)	2.95 (0.58)	2.98 (0.57)	3.53 (0.86)	3.57 (0.83)	3.75 (0.74)	3.78 (0.74)
95% CI	–0.33, 0.09	–0.37, –0.01	–0.29, 0.07	–0.35, 0.01	–0.29, 0.03	–0.34, 0.17	–0.44, 0.05	–0.44, 0.03	–0.36, 0.10
Employment
Academic	2.73 (0.80)	3.07^* (0.70)	2.78 (0.62)	2.98 (0.65)	3.03 (0.64)	3.63 (0.91)	3.69^* (0.90)	3.93^* (0.81)	3.90 (0.73)
Non-	2.57 (0.62)	2.74^* (0.52)	2.79 (0.56)	2.82 (0.55)	2.87 (0.49)	3.45 (0.79)	3.39^* (0.75)	3.56^* (0.72)	3.65 (0.73)
95% CI	–0.42, 0.08	–0.55, –0.12	–0.21, 0.23	–0.38, 0.05	–0.36, 0.04	–0.49, 0.13	–0.60, –0.01	–0.65, –0.09	–0.53, 0.03
Smoking
No	2.66 (0.65)	2.87 (0.57)	2.80 (0.56)	2.87 (0.59)	2.95 (0.53)	3.53 (0.83)	3.49 (0.84)	3.70 (0.75)	3.74 (0.74)
Yes	2.51 (0.67)	2.70 (0.59)	2.75 (0.60)	2.82 (0.55)	2.82 (0.51)	3.40 (0.77)	3.39 (0.71)	3.52 (0.74)	3.64 (0.73)
95% CI	–0.06, 0.36	–0.02, 0.35	–0.13, 0.24	–0.14, 0.23	–0.04, 0.29	–0.13, 0.39	–0.16, 0.36	–0.06, 0.42	–0.13, 0.34
Exercise
No	2.62 (0.65)	2.82 (0.55)	2.78 (0.53)	2.87 (0.57)	2.89 (0.49)	3.52 (0.77)	3.46 (0.76)	3.64 (0.73)	3.73 (0.70)
Yes	2.50 (0.74)	2.77 (0.75)	2.80 (0.78)	2.78 (0.63)	3.02 (0.69)	3.25 (1.03)	3.45 (0.99)	3.60 (0.87)	3.56 (0.92)
95% CI	–0.17, 0.41	–0.21, 0.30	–0.27, 0.24	–0.17, 0.34	–0.37, 0.10	–0.09, 0.63	–0.34, 0.36	–0.29, 0.38	–0.16, 0.50

Note. M: mean average, SD: standard deviation, 95% CI: 95% confidence interval.

The results of the independentt-tests showed that employment status had a statistically significant effect on three HLQ scales. Academic employees had a higher mean average than non-academics on the scales “Having sufficient information to manage my health,” “Navigating the healthcare system,” and “Ability to find good health information” (Table 5). Participants’ gender had a statistically significant effect only on one HLQ scale; females had a higher mean average than males on the “Having sufficient information to manage my health” scale.

4 Discussion

University employees experience a variety of work-related stressors that endanger their health and well-being. High levels of stress and being sedentary for a significant amount of time are two main health risks experienced by university employees. The negative impact of such health problems necessitates seeking long-term solutions to optimize the health of university employees. The literature shows that health literacy is a key predictor of health outcomes among different populations [13 –15]. However, university employees’ health literacy is still an evolving research area with mixed results regarding the role of sociodemographic characteristics of university employees in determining health literacy. Therefore, the authors of this article intended to gain an in-depth understanding of this phenomenon using a valid, comprehensive tool to measure health literacy (i.e., the HLQ). The Cronbach’sα values reported in this study support the internal consistency of the Arabic HLQ and are consistent with previous findings regarding the original and translated versions [33 , 36–38].

The results revealed that the lowest mean averages of HLQ scales among all participants were “Feeling understood and supported by healthcare providers” (Part 1 of the HLQ) and “Ability to actively engage with healthcare providers” (Part 2 of the HLQ). Limited health literacy was reported as a prominent problem in previous studies conducted among university employees and other age groups [22 –24]. Such issue is considered an obstacle to make appropriate health-related decisions. Consequently, limited health literacy acts as a potential source of health disparity and/or a significant predictor of health outcomes because it hinders university employees’ ability to manage commonly reported current (e.g., headache and back pain) and chronic (e.g., heart disease) health problems [4–6 , 12]. Improving the level of university employees’ health literacy could help optimizing the health outcomes both at the workplace and in other settings [21].

MANOVA results showed that university employees’ age, level of education, and BMI had a statistically significant effect on the linear composite of the HLQ scales. Besides, the interaction of all independent variables had a statistically significant effect on the linear combination of the HLQ scales. These findings are indicative of the role of sociodemographic characteristics and nutritional status in determining the level of health literacy among university employees. Addressing such sociodemographic variations is essential to establish effective health literacy interventions at workplace settings [15]. In addition, acknowledgement of the individual variations and the diversity of the university employees is important to reach the largest number of audience and accomplish the maximum level of effectiveness of health literacy interventions [15 , 24]. In other words, appraising the role of the sociodemographic variations in determining the health literacy of university employees necessitates planning and implementing various delivery approaches of the health literacy interventions.

As mentioned earlier, the literature regarding the role of sociodemographic variables in determining university employees’ health literacy provides mixed results. Consistent with the findings of the current study, university employees’ health literacy was affected by age and education [25]. Karl and McDaniel found that even well-educated university employees have limited health literacy [24]. Studies conducted among populations other than university employees also reported an association between the level of education and health literacy [39, 40]. Multivariate analysis regarding the role of gender was not statistically significant, a finding that is similar to the one reported in a study conducted among Iranian university employees [25]. Regarding university employees’ smoking and regular exercise, no previous research was found to compare the current study results with the literature. However, a study conducted among the general population reported a lack of association between smoking and health literacy [39].

Despite the lack of multivariate statistical significance pertinent to employment status, the results of the independentt-test showed that academic employees had higher scores on three HLQ scales compared to non-academic employees. These three scales are “Having sufficient information to manage my health,” “Navigating the healthcare system,” and “Ability to find good health information.” This finding requires further investigation as it addresses the possibility of having a health literacy disparity between academic and non-academic employees. Alkhatib reported that university employees’ sedentary risk factors exist regardless of the employee job role (i.e., academic or non-academic) [41]. Considered collectively, non-academic employees could be at higher risk for experiencing negative consequences of sedentary time as they have lower levels of some domains of health literacy. Further research is warranted to understand this phenomenon better while addressing important sociodemographic variations like educational attainment.

No previous research about the effect of BMI on health literacy in university employees was identified; therefore, comparison with other results was not possible. According to the multivariate and follow-up analyses results, BMI had a statistically significant effect on the level of health literacy. The effect was detected on six HLQ scales. More specifically, university employees with normal (healthy) weight had higher mean average scores on these scales than overweight employees (Table 4). According to Xie and colleagues, who conducted a study among rural residents in China, higher BMI levels predict lower health literacy scores [40]. Our finding regarding the university employees’ BMI is also consistent with the results reported in a Danish, population-based study [39]. In the Danish study, both multivariate and univariate analyses revealed a statistically significant association between BMI and health literacy. Additionally, the level of health literacy of was positively associated with improved diet quality and better food choices [21].

Health literacy plays a significant role in the ability of individuals to find and manage complex health information [34, 42]. During the COVID-19 pandemic, the need for health information has increased with a significant increase in the number of people searching for such information from different sources [43]. Therefore, people around the world have also faced an “infodemic”, an overabundance of correct and incorrect information [42]. Having an adequate level of health literacy increases the awareness of finding reliable resources of health information. In addition, health literacy enables the individuals to appraise the health information and utilize them to make informed health-related decisions. The authors of the current study did not specifically examine the relationship between health literacy and university employees’ ability to find, appraise, and utilize accurate information pertinent to the COVID-19 pandemic. However, the evidence in the literature suggests that people with limited health literacy relied more frequently on social media to find information about the COVID-19 pandemic [42, 43].

4.1 Implications

While the results reported here are discrepant with the results reported in other relevant studies, key implications should be discussed. This is one of the very few health literacy studies conducted among university employees. The campus community is a suitable environment to implement health promotion programs [44]. Many university employees are in positions where they are considered role models to other people (students and colleagues) within the campus community. The findings reported in this article provide evidence that variations in health literacy levels are attributed to individual characteristics of university employees. Addressing such variations by occupational healthcare providers is important to build successful health promotion interventions.

4.2 Limitations

The generalizability of the current study findings could be limited due to the sampling method, non-probability sampling, and data collection from employees at a single university in Jordan. Even though a robust statistical test was used (Roy’s Largest Root), the generalizability is also limited by conducting the analyses on non-equivalent groups of university employees. Other limitations of this study include the cross-sectional design and relying on self-report to collect the data. Conducting longitudinal studies with different points of time for data collection is recommended. In addition, the authors recommend relying on other objective indicators of health in future research. The authors also investigated the role of several sociodemographic and nutritional status in determining university employees’ health literacy. However, investigation of other variables is warranted to gain more insights regarding health literacy determinants among university employees.

5 Conclusion

The current study results showed that university employees’ sociodemographic characteristics play a significant role in determining health literacy. Three variables (university employees’ age, education, and BMI) had a significant effect on the level of health literacy. The interaction effect of all independent variables on the university employee’s health literacy was statistically significant. Univariate and follow-up analyses also revealed that different dimensions of health literacy vary based on university employees’ characteristics. These results highlight the need for addressing such characteristics as a source of disparity in university employees’ health literacy. To improve the health literacy of university employees, such variations need to be addressed in health-promoting interventions.

Ethical approval

This study was approved by the Institutional Review Board (IRB) committee at Jordan University of Science and Technology (IRB# 603-2017).

Informed consent

Written informed consent was obtained from each participant before asking the employees to complete the data collection questionnaires.

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Acknowledgments

The authors would like to thank the participants for their time and effort participating in this study.

Funding

This study was fully funded by the Faculty of Scientific Research at Jordan University of Science and Technology (Grant ID: 82-2018).

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