Health Professionals' Readiness and Its Associated Factors to Implement Telemedicine System at Public Hospitals in Harari Region, Eastern Ethiopia, 2024: A Cross‐Sectional Study

ABSTRACT

Background and Aims

Telemedicine enhances healthcare access in resource‐limited settings. In Ethiopia, despite its potential, implementation remains limited due to healthcare professionals' readiness. Therefore, this study aimed to assess the readiness of healthcare professionals for telemedicine implementation and its associated factors in public hospitals in the Harari region, Eastern Ethiopia, 2024.

Method

An institution‐based cross‐sectional study was conducted from August 5–15, 2024, among randomly selected 383 health professionals at public hospitals in the Harari region. Data were collected using a structured, self‐administered questionnaire assessing readiness, attitudes, digital competencies, and sociodemographic factors. The primary outcome was readiness for telemedicine implementation. Data were entered into EpiData version 4.6 and analyzed using STATA 17.0. Bivariate and multivariable logistic regression were used to identify factors associated with readiness.

Results

Readiness to implement telemedicine was reported by 57.5% of health professionals (95% confidence interval (CI): 52.3–62.6). Having favorable attitude towards telemedicine (Adjusted odds ratio (AOR) = 2.61, 95% CI (11.63–4.15), computer skills (AOR = 4.07; 95% CI: 2.15–7.55), computer literacy (AOR = 3.00, 95%, CI (1.51–5.97), educational status (AOR = 4.84, 95% CI (1.31–17.79), work experience (AOR = 2.34, 95% CI (1.06–5.17) significantly associated with health professional's readiness for the implementation of telemedicine.

Conclusion

More than half of the respondents demonstrated readiness for the implementation of telemedicine. Digital skills, educational level, work experience, and attitude largely determined readiness. These findings support targeted capacity‐building interventions to enhance digital literacy and promote positive perceptions, thereby accelerating telemedicine adoption in Ethiopian healthcare settings.

1. Introduction

The rapid advancement of digital technologies has transformed healthcare delivery worldwide, with telemedicine emerging as a critical tool to enhance access, efficiency, and quality of care, particularly in resource‐limited settings [1]. Its relevance has been especially pronounced during global crises such as the COVID‐19 pandemic, where remote consultations and digital health platforms ensured continuity of care despite physical barriers [2].

The World Health Organization defines telemedicine as the delivery of healthcare services at a distance, either through provider‐to‐provider interaction for clinical consultation and support, or client‐to‐provider, where patients access remote care [3].

Integrating telemedicine into healthcare systems strengthens service delivery, majorly in regions with limited access to specialized care [4]. In low‐ and middle‐income countries (LMICs) like Ethiopia, where healthcare resources are scarce and unevenly distributed, telemedicine offers a solution by extending clinical expertise to remote and underserved populations [5]. For sub‐Saharan Africa with 24% of the global disease burden, this innovation is critical [6]. Ethiopia, with just 0.96 health workers per 1000 people, is among the most affected [7]. Public hospitals in Ethiopia face significant challenges, including limited infrastructure and high patient loads, which could be alleviated through effective telemedicine adoption [8].

Despite its potential, the successful adoption of telemedicine relies largely on the readiness of healthcare workers, as they are the primary users responsible for integrating digital health technologies into clinical practice [9]. Gaining the support and readiness from healthcare professionals is essential to ensuring the implementation of telemedicine services [10]. The review identified staff‐related barriers, including technical challenges, reluctance to adopt new practice, insufficient preparedness, communication difficulties, suboptimal system design, and heightened staff anxiety [11]. Limited infrastructure, inadequate training, concerns about data security, and uneven digital literacy among healthcare workers could pose significant challenges [4, 5, 11].

In 21 sub‐Saharan African countries, mobile health interventions have taken diverse forms; the majority were telemedicine‐based (30%) and app‐based (29%), followed by SMS text‐based solutions (22%) [12]. Ethiopia's 2014 eHealth Strategy identified telemedicine as a key pillar of digital transformation, yet implementation remains uneven [13].

A recent bibliometric review highlights a persistent underrepresentation of readiness studies from LMICs, particularly in sub‐Saharan Africa [14]. This underscores the need for empirical research that bridges individual and institutional readiness and informs context‐specific implementation strategies. While existing literature predominantly focuses on technological and infrastructural challenges, it overlooks the human factors shaping telemedicine success [15, 16, 17]. Without a clear understanding of these readiness factors, efforts to scale telemedicine may face barriers, ultimately affecting patient care and healthcare system efficiency [9]. Despite Ethiopia's early commitment to digital health through its 2014 eHealth Strategy, there remains a lack of empirical data on healthcare professionals' readiness to implement telemedicine and the determinants of that readiness. To address this gap, the present study aimed to assess the level of telemedicine readiness among health professionals in public hospitals of the Harari region, Ethiopia, and to identify key predictors of readiness. Based on prior findings from similar low‐resource settings, we hypothesized that digital literacy, positive attitudes, and professional experience would significantly predict readiness for telemedicine implementation.

2. Methods

2.1. Study Population and Setting

An institution‐based cross‐sectional study was conducted from August 5–15, 2024, in Harari region public hospitals, eastern Ethiopia. Harar is located 525 km away from Addis Ababa, the capital of Ethiopia. In the Harari Region, there are two public hospitals, one police hospital, and one private hospital, 40 private clinics, and eight health centers. Hiwot Fana Comprehensive Specialized University Hospital (HFCSUH) with 585 total health professionals, and Jugal General Hospital, with 294 total health professionals (Harari Regional Health Bureau). The source population comprised all healthcare professionals working in these public hospitals. The study population consisted of healthcare professionals who were randomly selected from HFCSUH and Jugal General Hospital to participate in the survey.

2.2. Eligibility Criteria

Healthcare professionals with a minimum of 6 months of work experience were eligible for inclusion in this study. Individuals who were away from office due to leave or training during the data collection period were excluded.

2.3. Sample Size Determination

Single population proportion formula was used to determine the sample size.

$$\mathit{n}=\frac{{\left({\mathit{Z}}_{\mathbf{\alpha }/\mathit{2}}\right)}^{\mathit{2}}\ast \mathit{P}(\mathbf{1}-\mathit{P})}{{\mathit{d}}^{\mathit{2}}}$$

where: Zα/2: 1.96 (95% confidence level), d: 5% margin of error (0.05) P: estimated proportion of 65.4% of medical professionals' readiness to adopt telemedicine, taken from a previous similar study conducted in the Amhara region, Ethiopia [18]. After adding 10% for non‐response rate and the final sample size was 383.

2.4. Sampling Techniques

There are two public hospitals in the Harari region. One is general hospital and the other is comprehensive specialized hospital. The total number of healthcare professionals in the two hospitals is 879. Based on the number of health professionals, the calculated sample size was proportionally allocated to each public hospital. The study participants were selected using a computer‐generated simple random sampling technique (Figure 1).

Figure 1.

Sampling procedure for selecting the study participants at public hospitals in Harari region, Ethiopia, 2024.

2.5. Data Collection Tool and Procedure

The data were collected through a structured self‐administered questionnaire adapted from existing literature [18, 19, 20, 21, 22]. The questionnaire included five sections on sociodemographic characteristics, organization‐related factors, personal‐related factors, core readiness, engagement readiness, knowledge, and attitude towards telemedicine. The questionnaire was prepared in English because the study's participants were well‐educated and capable of understanding it. Data collectors included two BSc nurses and one master's holder who served as supervisors. The reliability was also checked using Cronbach's alpha coefficient (overall Cronbach's alpha for health professionals' readiness = 0.71).

2.6. Study Variables

2.6.1. Dependent Variable

Health professionals' readiness (intersection of core readiness and engagement readiness).

2.6.2. Independent Variable

Sociodemographic characteristics include age, sex, work experience, educational status, and profession. Organizational factors such as computer access at the office, internet access at the office, computer training, available IT support, backup power generator, and technical factors such as computer literacy, computer skills, knowledge, and attitude.

2.7. Operational Definition

Core readiness is defined as the realization of needs and expressed dissatisfaction with the current way of working [21, 23]. A respondent who scored 50% or more for the core readiness item (4 items) is assumed to have core readiness [24, 25].

Engagement readiness was assessed by health professionals based on the potential benefits and willingness to use the telemedicine system [21]. A respondent who scored 50% or more for the engagement readiness item (11 items) is assumed to have core readiness [24, 25]. The overall readiness of healthcare professionals is determined by the intersection of core and engagement readiness [18, 25].

Knowledge about telemedicine was assessed using 10 items, and the total scores were computed. Health professionals who correctly answered above or equal to 50% are considered to have good knowledge of telemedicine, and below 50% are considered to have poor knowledge [22].

Attitude towards telemedicine: Good health professional's attitude of telemedicine was defined as more than 50% score of the perceived relative advantage, compatibility, complexity, trialability, and observability of telemedicine. was assessed by questions answered on a 5‐point Likert scale that ranged from “1 = strongly disagree” to “5 = strongly agree”. In this study, a mean score of less than 11.5 (50%) was labeled as an unfavorable attitude, and 11.5 or more (50%) was labeled as a favorable attitude [22].

Computer literacy was defined as the ability to access, communicate, and process basic digital information for informed health decisions. This was measured using a 5‐point Likert scale, and respondents scoring at or above the median were considered to have adequate computer literacy; those below were classified as inadequate [26].

2.8. Measurement Tools

Knowledge of telemedicine was assessed using 10 items, each to be answered with either “Yes” or “No.” A score of “1” will be given for “Yes” and “0” for “No.” One can score a minimum of 0 and a maximum of 10 in this section. The total score ranged from 0 to 10 [22].

Attitude was assessed using items that captured five constructs from the Diffusion of Innovation framework: relative advantage, compatibility, complexity, trialability, and observability. Each item was rated on a 5‐point Likert scale (1 = strongly disagree to 5 = strongly agree). The total attitude score, therefore ranged from 5 to 25 [22].

2.9. Data Quality Control

To ensure data quality, data collectors and supervisors received 1 day of training covering procedures, ethics, and confidentiality. A pretest was conducted on 5% of the sample to identify and resolve ambiguities. Each questionnaire was assigned a unique ID, and data were stored securely without personal identifiers. Completeness checks and error detection control were applied before entry. Double data entry was performed by two clerks to enhance accuracy.

2.10. Data Processing and Analysis

The collected data were entered using Epi‐Data version 4.6 and analyzed using STATA 17.0. Descriptive analysis, such as simple frequencies and measures of central tendency, was used to summarize findings. Bivariate analysis was conducted using binary logistic regression to assess the association between independent variables and outcome variables. Variables with a p‐value of < 0.25 at a 95% confidence interval (CI) in the bi‐variable analysis were included in the multivariable logistic regression analysis. Adjusted odds ratio (AOR) with 95% CI was used to determine the factors associated with readiness, as it conveys the magnitude as well as precision of the effects. Multicollinearity was assessed using the variance inflation factor (VIF), showing no issues. The Hosmer–Lemeshow test was used to assess the model's goodness of fit. Statistical terms, abbreviations, and symbols are defined in the text and tables for clarity. All statistical tests were two‐sided, and statistical significance was set at p < 0.05.

2.11. Ethical Consideration

The approval letter for the study was obtained from the Institutional Health Research Ethics Review Committee of Haramaya University (Ref No: IHRERC/195/2024). Letters of support were provided to each public hospital, and permission was obtained from hospital administrators. The study adhered to the Declaration of Helsinki. Both verbal and written informed consent were obtained before data collection; participants were informed about the study's purpose and objectives. To protect the informant's confidentiality, no identifying information about them was included in this paper. Every participant's privacy was kept by giving their own code which was not known to any other person.

3. Results

3.1. Sociodemographic Characteristics

Out of 374 health professionals surveyed (a 97.6% response rate), just over half were female (53.48%). The mean age was 32.1 years (SD + 4.69), and the majority (51.6%) were between 30 and 34 years old. The majority of respondents (81.55%) have a work experience of more than 2 years, and 80.21% were BSc degree holders (Table 1).

Table 1.

Socio‐demographic characteristics of health professionals working in public hospitals, eastern Ethiopia, 2024 (N = 374).

Variables	Categories	Frequency (N)	Percentage (%)
Sex	Male	174	46.5
	Female	200	53.5
Age	20–24	7	1.9
	25–29	92	24.6
	30–34	193	51.6
	> =35	82	21.9
Profession	Medical doctor	79	21.1
	Midwifery	67	17.9
	Nurse	138	36.9
	Pharmacist	46	12.3
	Medical Laboratory	44	11.8
Work experience	< 2	52	13.9
	2–3	160	42.9
	4–5	57	15.2
	> 5	105	28.1
Educational status	Diploma	17	4.6
	Degree	300	80.2
	Masters and above	57	15.2

3.2. Organizational Factors

Regarding digital infrastructure, 260 health professionals (69.5%) reported having internet access at their office. However, only 62 participants (16.6%) indicated the availability of an IT support service, and 138 respondents (36.9%) reported the presence of a backup power generator at their workplace. Approximately 255 (68.18%) of the study participants had computer access at their offices (Table 2).

Table 2.

Technical and organizational factors towards telemedicine readiness among health professionals at public hospitals in Harari region, 2024.

Variables	Categories	Frequency (N)	Percentage (%)
Internet access at the workplace	Yes	260	69.5
	No	114	30.5
Available IT support workplace	Yes	62	16.6
	No	312	83.4
Computer Training	Yes	138	36.9
	No	236	63.1
Having computer access at the office	Yes	255	68.2
	No	119	31.8
Backup power generator	Yes	138	36.9
	No	236	63.1

3.3. Technical and Personal Factors

Among the study participants, 286 health professionals (76.47%) were not considered computer‐literate. Approximately 302 (80.75%) of the study participants possessed computer skills and 221 (59.1%) reported owning a personal computer. In this study, 206 (55.08%) of the study participants had inadequate knowledge of the telemedicine system. Similarly, 199 (53.21%) of the study participants had a favorable attitude toward telemedicine (Table 3).

Table 3.

Technical and personal factors towards telemedicine readiness among health professionals at public hospitals in Harari region, 2024.

Variables	Categories	Frequency (N)	Percentage (%)
Computer skill	Yes	302	80.8
	No	72	19.2
Computer literate	Yes	88	23.5
	No	286	76.5
Having a personal computer	Yes	221	59.1
	No	153	40.9
Computer Training	Yes	138	36.9
	No	236	63.1
Knowledge	Adequate	168	44.9
	Inadequet	206	55.1
Attitude	Favorable	199	53.2
	unfavorable	175	46.8

3.4. Health Professionals' Readiness Towards the Telemedicine Implementation

Among the total respondents, 52.94% had core and 51.87% engagement readiness. In total, 215 respondents (57.49%, 95% Cl, (52.0–62.0) were deemed ready for telemedicine adoption (Figure 2).

Figure 2.

Core and engagement, as well as overall readiness of health professionals towards telemedicine implementation in the Harari Region, Ethiopia, 2024.

Readiness scores varied slightly across professional categories. Nurses demonstrated the highest mean score (M = 57.15, SD = 4.44), followed by laboratory technologists (M = 56.84, SD = 5.34), midwives (M = 56.69, SD = 3.44), medical doctors (M = 56.52, SD = 4.33), and pharmacists (M = 56.43, SD = 6.63). Although the differences in mean scores were modest, the standard deviations suggest varying levels of consistency within each group, with midwives showing the most uniform readiness and pharmacists the most variability (Table 4).

Table 4.

Telemedicine implementation readiness scores by profession among health workers in Harari region.

Profession	Mean Score	Standard Deviation
Medical Doctor	56.52	4.33
Midwifery	56.69	3.44
Nurse	57.15	4.44
Pharmacist	56.43	6.63
Laboratory Technologist	56.84	5.34
Total	56.81	4.68

3.5. Factors Associated With Telemedicine System Readiness

In the bivariate analysis, variables that showed a p‐value less than 0.25, including computer access at home, computer training, attitude towards telemedicine, computer skills, computer literacy, work experience, and educational status, were candidates for multivariable analysis.

After adjusting for potential confounders in the multivariable analysis, health professionals with a favorable attitude were 2.6 times more likely to be ready for the telemedicine system than those with an unfavorable attitude towards telemedicine (AOR = 2.61, 95% CI: 1.63–4.15). Respondents with good computer skills were significantly more likely to be ready for a telemedicine implementation (AOR = 4.07; 95% CI: 2.15–7.55). Health professionals with adequate computer literacy were 3 times more likely to be ready for the telemedicine system than those with inadequate computer literacy (AOR = 3.00, 95% CI: 1.51–5.97). Respondents who had a master's educational level were 4.8 times more likely to be ready for the Telemedicine system than those who had a diploma (AOR = 4.84, 95% CI:1.31–17.79). Study participants who had more than 5 years of work experience were about 2.34 times more ready for the telemedicine system as compared with those study participants who had less than 5 years of work experience (AOR = 2.34, 95% CI:1.06–5.17) (Table 5).

Table 5.

Bivariate and multivariate analysis on factors associated with readiness of health professionals for the telemedicine system in the public hospital of Harari region, Ethiopia, 2024.

Variable	Readiness		COR (95%CI)	AOR (95%CI)	P‐value
	Ready	Not Ready
Attitude towards Telemedicine
Favorable	137	62	2.74 (1.80–4.19)	2.61 (1.63–4.15)	< 0.001
Unfavorable	78	97	1	1
Computer‐related skills
Yes	193	109	4.02 (2.3–7.0)	4.03 (2.15–7.55)	< 0.001
No	22	50	1	1
Computer training
Yes	92	46	1.83 (1.18–2.84)	0.97 (0.55–1.71)	0.93
No	123	113	1	1
Computer access at home
Yes	138	83	1.64 (1.08–2.49)	0.87 (0.52–1.47)	0.61
No	77	76	1	1
Computer literacy
Adequate	70	18	3.78 (2.14–6.67)	3.00 (1.51–5.97)	0.002
Not Adequate	145	141	1	1
Educational status
Diploma	7	10	1	1
Degree	162	138	1.67 (0.67–4.52)	1.67 (0.53–5.18)	0.37
Masters and above	46	11	5.97 (1.85–19.22)	4.84 (1.31–17.79)	< 0.001
Experience
< 2	20	32	1	1
2–3	96	64	2.4 (1.26–4.56)	2.29 (1.07–4.76)	0.03
4–5	32	25	2.04 (0.95–4.40)	1.74 (0.74–4.09)	0.20
> 5	67	38	2.82 (1.42–5.60)	2.34 (1.06–5.17)	0.04

Note: Model Fit Statistics: Hosmer‐Lemeshow p = 0.48.

4. Discussion

This study assessed health professionals' readiness for the implementation of telemedicine. Overall, 57.49% of participants were found to be ready for the implementation of telemedicine. Key factors associated with readiness included attitude towards telemedicine, computer skills, computer literacy, work experience, and educational status.

The overall readiness among health professionals was found to be 57.49% [95% CI: (52.29–62.55)], with core and engagement readiness observed at 52.94% and 51.87%, respectively. These findings were comparable to the studies conducted in the Amhara region, Ethiopia [18]. However, it is higher than the 33% readiness documented in Nigeria [27] and lower than the 86.3% in China [28]. These disparities emphasize the importance of training programs, policy enhancements, and technological infrastructure development to strengthen telemedicine readiness [4]. Socioeconomic factors, including patient‐provider trust and cultural preferences for in‐person care, also play a role in shaping telemedicine engagement [29]. These findings have direct implications for Ethiopia's Digital Health Strategy (2020–2025), which emphasizes workforce capacity‐building, interoperability, and equitable access to digital services.

The readiness gaps identified, particularly in core and engagement domains, highlight the urgent need for tailored interventions. These should include targeted training programs, strategic policy reforms, and infrastructure investments designed to address profession‐specific barriers and accelerate the effective implementation of telemedicine across diverse healthcare settings.

Respondents with a favorable attitude towards telemedicine were ready for the telemedicine implementation. This finding aligns with previous research indicating that attitude is a critical predictor of telemedicine readiness, as providers who perceive telemedicine as beneficial are more motivated to adopt and engage with the technology [18, 28]. Those with a favorable attitude toward telemedicine are more likely to be open to learning and integrating digital health solutions into their practice [30]. Their willingness to embrace technology fosters a proactive approach to training, troubleshooting, and engaging with telemedicine platforms, ultimately enhancing their readiness [9]. These findings show the importance of working with psychological and perceptual factors alongside technical and infrastructural requirements when implementing telemedicine systems.

Healthcare professionals with good computer skills were more likely to be ready for the implementation of telemedicine. This finding is consistent with other studies [18, 31]. This may be because familiarity with computer technologies and technical skills enables health professionals to actively adopt telemedicine, making it easier for them to adapt to new technologies [9].

Health professionals who had adequate computer literacy were more likely to be ready for the telemedicine system compared to those who had inadequate computer literacy. This finding is consistent with other studies [18, 31]. Adequate computer literacy enables professionals to efficiently operate telemedicine platforms, troubleshoot technical issues, and engage with virtual consultations, ultimately improving patient care and system efficiency. On the other hand, limited digital skills can create barriers, leading to lower engagement, workflow disruptions, and reluctance to embrace telemedicine [32, 33]. This suggests that digital literacy directly impacts a provider's ability to navigate telemedicine platforms, interpret data, and engage in virtual consultations efficiently. Higher technological competency reduces hesitation, enhances confidence, and minimizes workflow disruptions, making telemedicine integration smoother [32, 34]. In the Ethiopian context, this highlights the urgent need to integrate digital literacy and computer training into pre‐service curricula and in‐service professional development programs. Strengthening computer skills among health workers could accelerate telemedicine adoption, reduce resistance to digital innovations. Policymakers should also consider investments in infrastructure, such as reliable internet connectivity and access to computers in health facilities, to ensure that technical skills can be effectively applied.

Respondents with a master's degree were more likely to be ready for the implementation of telemedicine than those who had a diploma. This aligns with existing literature suggesting that advanced education correlates with greater digital literacy, familiarity with technology, and openness to innovative healthcare solutions [35, 36]. These findings highlight the need for targeted training and simplified telemedicine interfaces to bridge the readiness gap among less‐educated populations, ensuring equitable access to digital healthcare services.

Participants with over 5 years of work experience showed greater readiness for the implementation of telemedicine. This finding aligns with prior research indicating that experienced healthcare professionals adapt more easily to telemedicine due to their clinical expertise and exposure to evolving technologies [37]. Moreover, those with longer experience may have been exposed to the limitations of current care delivery, such as insufficiencies, access barriers, and resource constraints, which could heighten their appreciation for the potential of telemedicine.

These findings have important implications for Ethiopia's eHealth Strategy (2014), which emphasized the integration of ICT into healthcare delivery and the development of a digitally competent health workforce. The moderate level of telemedicine readiness observed in this study, particularly gaps in core and engagement domains, highlights the need for structured, profession‐sensitive digital health training. We recommend that the Ministry of Health and regional health bureaus incorporate telemedicine readiness modules into national pre‐service and in‐service training programs. Future research should explore longitudinal changes in telemedicine readiness following implementation. Longitudinal studies could track shifts in core and engagement readiness over time, identify factors that sustain or hinder adoption, and evaluate the impact of targeted interventions.

4.1. Strengths and Limitations

The study employed a cross‐sectional design, which limits the ability to establish a causal relationship. Moreover, this study relied on self‐reported measures to assess health professionals' readiness, digital literacy, and telemedicine‐related skills. While this approach is practical for large‐scale surveys, it introduces potential biases. In particular, social desirability bias may have led some participants to overstate their preparedness or digital competence, especially in institutional settings where telemedicine is viewed favorably. Additionally, self‐assessment may not accurately reflect actual skill levels, leading to overestimation or underestimation of readiness. These limitations should be considered when interpreting the findings, and future studies may benefit from incorporating objective assessments or triangulating with supervisor evaluations or system usage data. Future research could benefit from a mixed‐methods approach and longitudinal design to explore readiness dynamics over time and capture nuanced perspectives that quantitative tools may overlook.

5. Conclusion

More than half of the respondents have a good level of overall readiness for adoption of the telemedicine system. Key factors significantly influencing readiness include attitude, computer skills, computer literacy, educational status, and work experience. These findings highlight the need for targeted interventions, including enhancing digital literacy and promoting positive attitudes, to facilitate telemedicine adoption. Addressing these areas can improve acceptance and support the effective integration of telemedicine into healthcare systems.

Author Contributions

Selamawit Hailu: conceptualization, writing – review and editing, visualization, validation, software, methodology, investigation, writing – original draft, formal analysis, supervision. Belay Negash: writing – review and editing, visualization, validation, software, methodology, investigation, writing – original draft, formal analysis, supervision. Dawit Kassaye: writing – review and editing, visualization, validation, software, methodology, investigation, writing – original draft, formal analysis, supervision. Nesredin Ahmed: writing – original draft, writing – review and editing, visualization, validation, methodology, software, formal analysis, project administration, resources, supervision, data curation. Haymanot Mezmur: conceptualization, writing – review and editing, visualization, validation, software, methodology, investigation, writing – original draft, formal analysis, supervision.

Funding

The authors received no specific funding for this work.

Disclosure

The lead author Nesredin Ahmed affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.