{"paper_id":"20009f28-a681-4510-862f-73237f419d66","body_text":"Perceived quality of pharmaceutical HIV services in a post-conflict setting: structural equation modelling in Tigray, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Perceived quality of pharmaceutical HIV services in a post-conflict setting: structural equation modelling in Tigray, Ethiopia Hafte Kebede, Paul Ward, Hailay Gesesew, Francesco Checchi, Lillian Mwanri, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6325865/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Background The HIV care services in Tigray have been severely impacted during and after the infamous Tigray conflict, which took place from November 2020 to November 2022. The present study assessed the perception of care through people living with HIV satisfaction towards pharmaceutical services in the post-conflict Tigray, North Ethiopia. Methods A cross-sectional survey using exit interviews was conducted, with data captured via Qualtrics XM Software. The study assessed overall satisfaction using 31 indicators across five latent dimensions: provider communication, commitment and respect, medication use information, solving drug problems, and pharmacy environment. Second-order structural equation modeling quantified how these interrelated factors collectively predict satisfaction. Model robustness was verified through fit indices, ensuring the reliability and validity of the findings. Results The study reveals low overall satisfaction (57.2%) among people living with HIV, with significant gaps in medication use information (49% satisfied) and solving drug problems (50.2%). Structural equation modeling identifies that improving medication use information has the highest impact on satisfaction (68.2% increase per quality unit, β = 0.682), followed closely by solving drug-related problems (64.4%, β = 0.644), provider communication skills (59.4%, β = 0.594), and commitment & respect (37.7%, β = 0.377), all statistically significant (p < 0.05). Facility type significantly influenced satisfaction, with health centers outperforming referral hospitals by 128% (β = 1.281, p < 0.001), while primary hospitals showed a drastic 83% decrease in satisfaction (β = -1.789, p = 0.003). Extending refill intervals beyond 3 months increased satisfaction odds 5.6-fold (β = 1.732, p < 0.001). Interestingly, non-Mekelle residents reported 79% higher satisfaction than Mekelle residents (β = 0.794, p = 0.028). The model explained 84.4% variance, with minimal demographic effects (p > 0.05). Conclusions Satisfaction with pharmaceutical services among people living with HIV in Tigray is significantly lower than the national benchmark of 85%, raising concerns about HIV outcomes. Enhancing drug therapy management and optimizing appointment spacing are essential strategies for improving satisfaction during post-conflict rehabilitation. Targeted interventions should prioritize addressing gaps in the provision of medication use information and drug related problem-solving capabilities, particularly in primary hospitals, where satisfaction levels are critically low. Health sciences/Health care/Health services/Rehabilitation Health sciences/Health care/Health policy antiretroviral therapy conflict HIV post-conflict structural equation modelling satisfaction Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Sub-Saharan Africa (SSA), bearing 70% of global HIV (human immunodeficiency virus) cases 1 , faces compounded healthcare challenges in conflict-affected regions where armed violence persistently disrupts antiretroviral therapy (ART) services. Recent evidence from Ethiopia 2 , Sudan 3 , South Sudan 4 , and the Democratic Republic of Congo (DRC) 5 demonstrates that military conflicts collapse pharmaceutical supply chains, destroy health infrastructure, and displace populations – resulting in people exceeding 80% without access to health care services, with fewer than 20% of individuals receiving ART. These systemic failures directly undermine the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets, manifesting clinically as increased viral loads 6-8 , higher mortality rate 9 , HIV drug resistance 10 , and secondary HIV transmission through survival-related risk behaviors 11 . The 2020–2022 Tigray war serves as a stark case study of healthcare devastation. During the war, 80% of health facilities were destroyed, leading to severe shortages of ART, with availability dropping by 68.8%. ART follow-up rates plummeted to 16.6%, and laboratory services for HIV/AIDS patients declined by 95.5%, affecting approximately 43,000 individuals 2 12 13 . Post-war recovery phases present unique challenges, as seen in Syria, Yemen, and South Sudan 4 14 15 , often hindered by destroyed infrastructure, healthcare worker shortages, and supply chain disruptions, keeping ART coverage below 20% 16-18 . In Tigray, post-war assessments revealed that 62% of people living with HIV (PLWH) faced ART interruptions lasting three months or longer, contributing to rising Non-nucleoside reverse transcriptase inhibitors (NNRTIs) resistance and an 80% decline in clinical follow-up 19 . HIV prevalence doubled to 3% 20 , while only 3% of health facilities had fully resumed operations by 2023 21 , worsening the public health crisis. These findings underscore the urgent need for health system reconstruction and quality assessments of HIV services. In post-conflict Tigray's fragile healthcare landscape, evaluating patient experiences with HIV care services becomes critical for rebuilding HIV care systems that address both clinical and psychosocial dimensions of post-conflict recovery. The post-conflict reconstruction of Tigray’s HIV care system demands patient-centered frameworks where satisfaction metrics critically mediate the relationship between service quality and clinical outcomes 22 . Evidence demonstrates satisfaction's multidimensional impact: provider-patient dynamics significantly influence outcomes: strong relationships increase care reengagement by 55% 23 , while each additional minute of consultation time raises satisfaction by 0.077 standard deviations (β=0.077) 24 . Trust in providers demonstrates a moderate but clinically meaningful effect on adherence (SMD=0.377) 25 , which directly enables viral suppression (<50 copies/mL) in 94% of adherent patients 26 . Adherence thresholds reveal stark outcome disparities: patients maintaining ≥95% ART adherence experience 22% treatment failure rates, versus 80% failure among those with <80% adherence (P<0.001), alongside 79% fewer hospitalization days (2.6 vs. 12.9 days/1000 follow-up; P=0.001) and elimination of opportunistic infection-related mortality 27 . Pharmaceutical services are pivotal in this cascade, reducing drug-related problems (DRPs) by 19% per patient (5.2 → 4.2; P=0.043) and boosting CD4+ counts by 51.3 cells/mm³ (P=0.015) through optimized therapy 28 . Pharmacist-led interventions systematically enhance outcomes, doubling adherence odds (OR=2.70) and quadrupling viral suppression rates (OR=4.13) 29 , while generating $51–$166 annual savings per patient with a 2.51:1 benefit-cost ratio) 30 . DRPs account for 59.9% of treatment failures (5.77 vs. 4.08 DRPs/patient in success cohorts) 31 , underscoring the necessity of integrated pharmaceutical care to sustain virologic control 27-29 32 . However, post-war Tigray lacks data on patient experiences with pharmaceutical services—a critical gap that impedes targeted interventions. Addressing this through satisfaction metrics could identify modifiable barriers (e.g., communication improvements shown to raise adherence odds by 62% (OR=1.62) 33 , restore healthcare trust, and align reconstruction efforts with patient needs for long-term epidemic control. This study assessed HIV care clients’ satisfaction with pharmaceutical services in ART clinics in post-war Mekelle City, Tigray, examining structural, procedural, and outcome-related care dimensions. Findings from the study provided actionable insights to improve ART adherence, retention, and treatment outcomes, offering context-specific recommendations for strengthening pharmaceutical services for people living with HIV (PLHIV) in Tigray and similar conflict-affected settings. Methods Study Design and setting This is an institution-based cross-sectional study conducted among ART patients at 11 health facilities (8 public and 3 non-governmental organization (NGO) managed) in Mekelle City, Tigray. These facilities offer comprehensive HIV services, including clinical evaluation, counseling, antiretroviral drugs, prophylaxis, management of opportunistic infections, and regular follow-up. All medications and services are fully funded by The U.S. President's Emergency Plan for AIDS Relief (PEPFAR) provided free of charge to patients 34 35 . Data collection took place from October 1 to 30, 2024. Source and Eligible Population The study population comprised adult people living with HIV in Mekelle City undergoing ART follow-ups. Eligible participants were individuals aged 18 or older who had been on ART for at least 12 months at selected health facilities as of October 2024, could understand Tigrigna, and consented to participate. Exclusion criteria included patients who did not consent, were critically ill, had received ART for less than a year, had uncontrolled psychiatric disorders, or were seeking emergency medical care. Sampling A total of 24 government and NGO managed facilities providing HIV services in Mekelle City were identified. Using the United States Agency for International Development (USAID) /Deliver Project’s Logistic Indicators Assessment Tool (LIAT) 36 , which recommends selecting at least 15% of facilities, 11 facilities (45.83%) were purposively selected based on patient load, facility level, service provision, and agreement to participate. These included 2 medium clinics, 4 health centers, 2 primary hospitals, 2 general hospitals, and 1 referral hospital, with one primary hospital and two clinics managed by NGOs. A sample size of 631 study participants was calculated using a single proportion formula assuming 95% confidence level, 5% margin of error, and 5% non-response rate 37 . Participants were recruited through consecutive sampling across selected facilities to assess satisfaction with HIV care services in post-conflict Mekelle City. The Conceptual Model This study applies Donabedian’s healthcare quality framework 38 to examine the relationship between pharmaceutical care quality domains and overall satisfaction with pharmaceutical services. The model categorizes quality into structure (pharmacy environment), process (provider communication, commitment and respect, medication use information, and solving drug problems), and outcome (overall satisfaction). The framework proposed that structural factors influence process elements, which in turn affect the outcome i.e. patient satisfaction. While Donabedian’s model provides a comprehensive framework, it overlooks potential antecedents of patient satisfaction, as noted by Coyle et al. 39 . To address this limitation, the study incorporates patient characteristics as a secondary determinant, integrating insights from the value expectancy model and multiple models’ theory 40 41 , which emphasize the role of patient expectations, social and cultural factors, and health status. This adapted model, tested through 12 hypotheses (H1-H12), offers a holistic approach to understanding and improving pharmaceutical service quality 38 41 42 . Figure 1 presents the proposed model, grounded in this theoretical framework and relevant literature. Hypotheses: H1: The pharmacy environment significantly affects provider client communication. H2: The pharmacy environment significantly affects provider attitude. H3: The pharmacy environment significantly affects provision of medication information. H4: The pharmacy environment significantly affects solving medication related problems. H5: Satisfaction among people living with HIV is positively correlated with their perception of the quality of provider-client communication. H6: Satisfaction among people living with HIV is positively linked to their perceived commitment and respect shown by healthcare providers H7: Satisfaction among people living with HIV is positively associated with their perceived quality and the adequacy of medication use information provided H8: Satisfaction among people living with HIV is positively linked to their perceived quality of providers' ability to address and resolve drug-related issues. Mediating Effects H9: Health facility type moderates the relationship between the pharmacy environment and provider-client communication. H10: Health facility type moderates the relationship between the pharmacy environment and provider attitude. H11: Health facility type moderates the relationship between the pharmacy environment and the provision of medication information. H12: Health facility type moderates the relationship between the pharmacy environment and drug-related problem-solving Data Collection Instruments To ensure contextual relevance, we conducted a comprehensive review of studies evaluating HIV care and pharmaceutical service quality dimensions in Ethiopia and sub-Saharan Africa 43-50 . The survey tools, initially developed in English, were rigorously translated into Tigrigna and back-translated for consistency. A two-phase field test involving 66 HIV-positive ART patients across 11 facilities assessed content validity and reliability. This process, which excluded participants from the main study to maintain data integrity, helped refine the questionnaire's language clarity and flow. The study instrument incorporated measures of sociodemographic characteristics and facility-related factors, adapted from the Ethiopia Demographic and Health Survey (DHS), alongside satisfaction predictors. Data Collection To ensure data quality, the questionnaire was encoded and prepared using Qualtrics XM software. Data collectors used smartphones or tablets to conduct exit interviews immediately after patients completed their clinical encounters. Each interview lasted 20–30 minutes and were recorded online through the software provided by Torrens University Australia. Eleven trained HIV care providers conducted the interviews. Training was delivered for two days two covering study objectives, data collection procedures, use of the Qualtrics XM software, and participant interview techniques. Data collector gathered facility-specific details from administrators, including facility type, services offered, managing authority, and functional status. To ensure ethical compliance, patients were informed about the study's purpose before voluntarily participating in the survey. Measurement of Variables The study assessed perceived HIV care through study participants' overall satisfaction with pharmaceutical services using 31 items across five key service quality dimensions: (1) provider communication, (2) commitment and respect by the provider, (3) provision of medication use information, (4) solving drug-related problems, and (5) pharmacy environment. Overall satisfaction was evaluated by categorizing responses as 'Satisfied' or 'Dissatisfied' based on the mean score. Clients rated their perceptions of service aspects on a 5-point Likert scale, ranging from 1 (extremely dissatisfied) to 5 (extremely satisfied). Supplementary file 5 summarizes the indicators used to construct the quality scales, providing a comparative basis for assessing service quality dimensions in the study setting. Operational Definitions Patient satisfactions: Refers to an individual study participants’ evaluation of the pharmaceutical care they receive in a health-care setting based on their experiences in their treatment journey. HIV Care Clients Experience: HIV care client experience refers to the interactions between HIV care services and the people living with HIV over at least 12 months. These interactions include dispensing, counseling, monitoring, and addressing medication-related issues. Level of Satisfaction: Based on the composite mean of the five points Likert scaled items analysis result, a mean score of > 3.57 was classified as “Satisfied” while those with mean score < 3.57 were classified as “Dissatisfied”. Other Measures: Study participants self-reported their gender, age, marital status, education level, income, and family size. Satisfaction indicators were assessed using a validated question: “In general, over the past 24 months, how satisfied are you with (service)?” Responses were recorded on a 5-point Likert scale, ranging from “Extremely Dissatisfied” to “Extremely Satisfied.” Data Analysis Satisfaction is a complex, multidimensional construct. To address this complexity, this study employed the Donabedian Framework 38 , and structural equation modeling (SEM) incorporating facility characteristics, socio-demographic attributes, and clinical factors. A two-step SEM approach was used, starting with a measurement model to identify latent variables from observed indicators. Data preprocessing involved listwise exclusion of incomplete responses (n = 20) and participants exhibiting insufficient variability (composite score range: 0.5–4.5; SD ≥0.25 threshold) (n = 50) to mitigate response bias. Categorical predictors underwent factorization with reference category specification, while nominal variables were orthogonal dummy-coded (k-1 contrasts). Normality assumptions were evaluated through quantile-quantile deviation plots, complemented by Shapiro-Wilk (W >0.95, P >0.05) and Kolmogorov-Smirnov (D <0.05, α=0.05) tests, with distributional metrics (skewness |<2|, kurtosis |<7|) confirming parametric conditions. The measurement model underwent confirmatory factor analysis (CFA) using a diagonally weighted least squares (DWLS) estimator, appropriate for ordinal/non-normal data. Seven items exhibiting psychometric inadequacies, including high cross-loadings (PC1, CR1) and suboptimal factor loadings (<0.6: PC3, MI6, MT4, MA1, IA4), were iteratively removed to achieve model parsimony (see supplementary file 2). Scale reliability met established benchmarks (Cronbach’s α ≥0.7), with convergent validity demonstrated through standardized regression weights (≥0.50), composite reliability (CR ≥0.7), and average variance extracted (AVE ≥0.5). Discriminant validity adhered to the Fornell-Larcker criterion, where AVE values exceeded squared inter-construct correlations (≤0.80). Prior to structural equation modeling, multicollinearity diagnostics identified three exogenous variables requiring exclusion due to excessive collinearity (VIF >10: Facility Type (55.18), Level of Care (13.55), Service Type (18.01)). The structural equation model quantified associations between service quality dimensions and overall satisfaction using standardized correlation coefficients (β). Direct, indirect, and total effects were computed to capture unmediated influences and mediation pathways. Path diagrams visualized relationships among five first-order constructs (provider communication, commitment & respect, medication use information, solving drug problems, and pharmacy environment) and the second-order construct of overall satisfaction. Model fit was assessed using multiple indices (WRMR ≤1.0, CFI >0.90, TLI >0.90, RMSEA ≤0.08) 51 52 , excluding χ² due to sample size sensitivity. Diagnostic checks, including multicollinearity tests and reliability assessments, ensured robustness. The Diagonally Weighted Least Squares (DWLS) estimator was employed to handle non-normal data, facilitating proper identification of key predictors in the model. The analysis of overall satisfaction used a composite score approach, calculating the mean of individual satisfaction indicators for each respondent across a total sample of 526 participants. These composite scores were categorized into \"Satisfied\" and \"Dissatisfied\" groups based on the sample's overall mean score (3.57), creating a binary outcome variable. Due to the non-normal distribution of the data, binary logistic regression was applied using generalized linear models (GLMs) to assess the effects of sociodemographic predictors on satisfaction. To improve robustness and account for participant-level variability, generalized linear mixed-effects models (GLMER) were also employed, incorporating random effects. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to measure the strength and significance of predictor effects. All analyses were performed using R (version 4.3.1). Ethical Consideration Ethical approval for this study was obtained from the Institutional Review Board of Tigray Health Research Institute(THRI) (THRI/4031/0502/16, 7 February 2024) and Torrens University Australia Human Research Ethics Committee(TUA-HREC) (Ethics Application 0333, 14 May 2024). The Tigray Region Health Bureau(TRHB) granted permission (Ref No. 2579/365/16, 12 March 2024). Participants were informed about the study's objectives, assured of confidentiality and their right to withdraw without consequences. Written or documented oral informed consent was obtained from all participants. RESULTS Overview of the Study Population The study recruited 631 participants, of whom 596 completed the survey, yielding a 94.4% response rate. After eligibility screening, 526 respondents (88.3% of those who responded) were retained for final analysis. The sample predominantly comprised middle-aged adults (66.5%), followed by older adults (24.3%). Females constituted the majority (60.6%) of participants, and 53.6% were married. Regarding education, most participants had completed primary school (38.2%), followed by those with secondary education (29.3%). In terms of employment, the largest group was self-employed (29.8%), closely followed by those employed in government or private sectors (27.6%). (supplementary file 1). Confirmatory Factor Analysis (CFA) outputs The confirmatory factor analysis (CFA) employing robust maximum likelihood estimation validated a theoretically derived measurement model comprising five latent constructs: provider communication, commitment and respect, medication use information, solving drug problems, and pharmacy environment. Global fit indices demonstrated exceptional alignment with the hypothesized structure comparative fit index (CFI) = 0.996; Tucker−Lewis Index (TLI) = 0.995, surpassing the 0.95 threshold for excellent fit. Residual analysis further supported model adequacy standardized root mean square residual (SRMR)=0.052, with approximate fit metrics root mean square error of approximation (RMSEA) = 0.080;90 indicating acceptable population-level discrepancy. All constructs exhibited strong psychometric properties, with standardized factor loadings exceeding 0.70 across indicators (p < 0.001), demonstrating statistically significant and substantively meaningful relationships between observed variables and their respective latent factors. Specific loading ranges included provider communication (0.819–0.933), commitment & respect (0.883–0.923), medication use information (0.806–0.886), solving drug problems (0.704–0.927), and pharmacy environment (0.821–0.935), collectively explaining 58–87% of item variance (R² = 0.58–0.87) (supplementary file 3). Internal consistency reliability, assessed via Cronbach’s α, exceeded the 0.70 benchmark for all scales (α = 0.88–0.91), with medication use information demonstrating optimal reliability (α = 0.91). Item retention was further justified through corrected item-total correlations (>0.50) and invariance testing confirming stability across model iterations. The CFA path diagram is plotted; see Figure 2 for details. Inter-factor correlations revealed theoretically coherent relationships, most notably between provider communication and commitment & respect (φ = 0.650, p < 0.001), while pharmacy environment showed weaker yet significant associations with other constructs (φ = 0.250–0.400). Critical covariances included provider communication ↔ solving drug problems (ψ = 0.917, p < 0.001) and medication use information ↔ solving drug problems (ψ = 0.897, p < 0.001), suggesting synergistic operationalization of these dimensions in shaping pharmaceutical service satisfaction. (See Table 1&2) Table 1: Covariances Among Latent Variables Latent Variables Estimate Covariance (R) P-value Provider Communication ↔ Commitment & Respect 0.639 0.852 <0.001 Provider Communication ↔ Medication Use Info 0.614 0.900 <0.001 Provider Communication ↔ Solving Drug Problems 0.695 0.917 <0.001 Provider Communication ↔ Pharmacy Environment 0.547 0.786 <0.001 Commitment & Respect ↔ Medication Use Info 0.574 0.753 <0.001 Commitment & Respect ↔ Solving Drug Problems 0.660 0.779 <0.001 Commitment & Respect ↔ Pharmacy Environment 0.650 0.835 <0.001 Medication Use Info ↔ Solving Drug Problems 0.691 0.897 <0.001 Medication Use Info ↔ Pharmacy Environment 0.470 0.664 <0.001 Solving Drug Problems ↔ Pharmacy Environment 0.562 0.715 <0.001 Table 2: Latent Variable Correlations Latent Variable Provider Communication Commitment and Respect Medication Use Information Solving Drug Problems Commitment and Respect 0.852 Medication Use Information 0.900 0.753 Solving Drug Problems 0.917 0.779 0.897 Pharmacy Environment 0.786 0.835 0.664 0.715 Structural Equation Modeling (SEM) outputs The second-order structural equation model, analyzed using the Diagonally Weighted Least Squares (DWLS) estimator to accommodate non-normal, ordinal data, demonstrated excellent fit to the observed data. Fit indices strongly supported the model's adequacy: Comparative Fit Index (CFI) = 0.992, Tucker-Lewis Index (TLI) = 0.991, Root Mean Square Error of Approximation (RMSEA) = 0.032 (90% CI: 0.026, 0.037), and Standardized Root Mean Square Residual (SRMR) = 0.060. While the χ² test was significant (p<0.001), this is expected given the large sample size (N=526) and does not necessarily indicate poor fit. The model exhibited robust psychometric properties, with all factor loadings exceeding 0.60 and achieving statistical significance (p < 0.001), ensuring strong convergent validity and reliability. Low residual variances further corroborated the model's strength, indicating that observed variables effectively captured their respective latent constructs. The latent variables, provider communication, commitment and respect, medication use information, solving drug problems, pharmacy environment, and overall satisfaction, all demonstrated statistically significant relationships with their observed indicators (p < 0.001). Notably, provider communication showed strong associations with its indicators (PC2, PC4, PC5, PC6), with standardized loadings ranging from 0.683 to 0.869, while commitment and respect exhibited loadings ranging from 0.761 to 0.865 for its indicators (CR2, CR3, CR4, CR5). (see supplementary file 4) Structural Relationships and Regression Coefficients The structural equation modeling analysis revealed significant positive effects of the pharmacy environment on various dimensions of pharmaceutical service provision in ART clinics in Mekelle City. The pharmacy environment showed a stronger association with commitment & respect (β = 0.868, p < .001) than with provider communication (β = 0.783, p < .001), exceeding Cohen's threshold for large effect sizes (β > 0.50) in behavioral research. These path coefficients suggest the pharmacy environment explains approximately 75.3% of variance in commitment & respect (R² = 0.868²) and 61.3% in provider communication (R² = 0.783²), reflecting its critical role in shaping interpersonal care dimensions. The positive directional relationships imply that each standard deviation improvement in environmental factors corresponds to 0.868 SD and 0.783 SD increases in commitment & respect and provider communication scores respectively (see table 3). The SEM path diagram is plotted; see Figure 3 for details. The hierarchical structural equation model positioned overall satisfaction as a second-order latent variable synthesized from four service quality dimensions, with medication use information demonstrating the strongest predictive power (β = 0.682, p < .001) implying that each standard deviation improvement in patients' understanding of medication use amplified satisfaction by 68.2% of a standard deviation. Solving Drug Problems (β = 0.644, p < .001) emerged as a critical operational lever, where systematic resolution of medication related problems contributed 64.4% of a SD satisfaction gain. Provider communication (β = 0.594, p < .001) highlighted the centrality of pharmacists' empathetic dialogue and jargon-free explanations, while commitment & respect (β = 0.377, p < .05) represented a foundational yet less influential expectation of professional decorum. Externally, extended appointment intervals (β = 0.366, p < .001) correlating with 36.6% higher satisfaction per SD increase in scheduling flexibility, whereas prolonged treatment duration eroded satisfaction by 16.7% per SD (β = -0.167, p = .011). The model's exceptional explanatory capacity (R² = 84.4%) was corroborated by first-order construct R² values spanning 80.5%-94.4%, with provider communication's 94.4% variance explanation (residual σ² = 0.040) and solving drug problems' 91.4% (σ² = 0.086) indicating near-complete capture of these dimensions' determinants. Table 3: Regression analysis assesses the relationships between latent variables and overall satisfaction, including demographic variables. Relationship Standardized Estimate (β) p-value Pharmacy Environment → Provider Communication 0.783 <0.001 Pharmacy Environment → Commitment & Respect 0.868 <0.001 Pharmacy Environment → Medication Use Information 0.644 <0.001 Pharmacy Environment → Solving Drug Problems 0.685 <0.001 Managing Authority → Overall Satisfaction -0.070 0.280 Appointment Interval → Overall Satisfaction 0.366 <0.001 Treatment Duration → Overall Satisfaction -0.167 <0.001 Marital Status → Overall Satisfaction -0.119 <0.001 Age → Overall Satisfaction 0.051 0.033 Gender → Overall Satisfaction 0.069 0.310 Binomial Regression Analysis of Pharmaceutical Service Satisfaction The binomial regression analysis revealed a polarized satisfaction landscape, with 57.2% of respondents reporting satisfaction with pharmaceutical services against 42.8% dissatisfaction. Service dimensions exhibited marked variability: commitment & respect achieved the highest satisfaction (76.4%), attributable to pharmacist availability, privacy adherence, and dignity in interactions, while pharmacy environment followed at 71.5%. Critical deficits emerged in provider communication (58.9% satisfied), medication use information (49%), and solving drug problems (50.2%), with dissatisfaction rooted in insufficient guidance on side effects (reported by 63% of dissatisfied respondents), drug interaction protocols, and missed-dose management strategies. The binomial regression analysis identified facility type as the strongest predictor of pharmaceutical service satisfaction, with health centers demonstrating substantially higher satisfaction levels compared to referral hospitals (β = 1.281, p < 0.001). Conversely, primary hospitals showed markedly lower satisfaction (β = -1.789, p = 0.003), equivalent to an 83% reduction in satisfaction odds relative to referral hospitals. Extended appointment intervals (>3 months) significantly enhanced satisfaction likelihood (β = 1.732, p < 0.001), translating to 5.6-fold greater odds compared to shorter intervals. Geographical disparities emerged with non-Mekelle residents reporting higher satisfaction (β = 0.794, p = 0.028). Demographic factors revealed nuanced patterns: middle-aged (β = -0.701, p = 0.11) and older adults (β = -0.844, p = 0.098) showed non-significant trends toward lower satisfaction versus young adults. Housing stability indicators approached significance, with patients living with family/relatives demonstrating marginally reduced satisfaction (β = -0.785, p = 0.053) compared to homeowners. Clinical history variables showed limited predictive power, as extended ART duration (>10 years) marginally reduced satisfaction (β = -0.433, p = 0.077) without achieving statistical significance. The model's non-significant intercept (β = 0.994, p = 0.185) indicates balanced baseline satisfaction across reference categories. Several table-specified predictors, including General Hospital status (β = -0.133, p = 0.686), rental housing (β = -0.145, p = 0.587), and Medium Clinic designation (β = 1.202, p = 0.133), failed to demonstrate statistical significance. (see Figure 4) Discussion The study highlights a complex interplay of factors influencing people living with HIV satisfaction with pharmaceutical services in a post conflict context using the case of Tigray. Pharmacies in Ethiopian public health facilities often struggle with high patient volumes, overcrowding, limited consultation spaces, medication shortages, and a lack of electronic record systems, contributing to patient dissatisfaction 53-55 . In contrast, ART clinics generally offer better-organized services with private consultation rooms, trained staff, and free of charge medications. However, our study found that satisfaction with pharmaceutical services in Mekelle City ART clinics (57.2%) was significantly lower than the satisfaction rates reported from outpatient pharmacies(60.4%–65.37%) 56-58 ; and ART service satisfaction levels in Ethiopia (70.7%–86.4%) 59-61 , and Cameroon (91.2%) 62 . Pre-war studies in Tigray also reported much higher satisfaction (75.2%–89.6%) 63 64 . Key areas of concern include low satisfaction with provider communication (58.9%), medication use information (49%), and solving drug problems (50.2%), all falling below the national benchmark of >85%. The primary reason for high dissatisfaction of PLWH stems from the prolonged disruption of HIV care during and after the Tigray War. Over 83% of patients were lost to follow-up during the conflict 12 , with many requiring transitions to second- or third-line ART regimens upon return 13 65 , which often have more severe side effects and necessitate frequent monitoring and shorter appointment intervals 66 . The healthcare system remains fragmented, with a significant loss of skilled providers and a surge in patient load, which has doubled from pre-war levels to 3% prevalence 20 which further hinder providers' ability to manage drug therapy, regimen changes, side effects, and adherence strategies. Furthermore, many HIV care providers were displaced due to the war 67 , remaining or newly appointed providers struggle with managing complex regimens, including Dolutegravir-based therapy, due to limited expertise. The study findings highlight that medication use information, solving drug problems, and provider communication are the most influential predictors of HIV care patients’ satisfaction with pharmaceutical services. Effective medication counseling, including adherence strategies, side effect management, and regimen education, significantly enhances patient satisfaction and adherence 68-70 which in turn impacts the HIV care outcomes including virological success and drug resistance 25-27 . Similarly, resolving issues such as adverse effects, drug interactions, and stockouts is essential for sustaining patient engagement and treatment continuity 71-73 . These findings underscore the importance of comprehensive pharmaceutical care in improving HIV treatment outcomes. The Pharmacy Environment also plays a pivotal role, indirectly influencing satisfaction by enhancing other dimensions such as Provider Communication, medication use information, and solving drug problems. A well-structured, patient-centered pharmacy setting featuring private consultation areas, efficient workflows, and welcoming designs ensures confidentiality, reduces stigma, and promotes open communication 57 74-76 . These elements are crucial for fostering trust and improving service experiences, which can lead to better adherence and health outcomes 77 78 . While commitment and respect had a relatively smaller impact, it remain an important contributor to satisfaction, aligning with prior research that emphasizes patient-centered care as a key determinant of HIV treatment success 79-81 . Strengthening these areas, in line with Ethiopia’s Compassionate, Respectful, and Caring (CRC) initiative, can improve patient-provider relationships, enhance compliance, and ensure continuity of care 38 82-84 . The study's findings have critical implications for HIV care in post-conflict Tigray. Low satisfaction with pharmaceutical services threatens ART adherence, increasing the risk of HIV drug resistance (HIVDR) and potentially reversing decades of progress in HIV care 85-87 . Disruptions in treatment continuity may lead to ongoing transmission, higher mortality rates, and costly interventions to re-engage lost patients. Additionally, the spread of resistant HIV strains poses a significant challenge to epidemic control efforts, further straining the already fragile healthcare system. Ensuring consistent treatment and patient retention is essential to achieving viral suppression and preventing further public health crises. Conclusion and Recommendations The HIV prevalence in post-conflict Tigray has doubled to 3%, underscoring the urgent need to strengthen HIV care services in post-conflict Tigray. This study reveals a mixed picture of patient satisfaction: while clients expressed relative satisfaction with provider-client interactions and pharmacy infrastructure, significant dissatisfaction persists in critical areas such as medication use information and drug-related problem-solving. These gaps, exacerbated by service disruptions and resource constraints, highlight the need for targeted interventions to improve care quality, treatment adherence, and patient well-being. Strengthening pharmacist training, optimizing workflows, and implementing structured refill schedules can enhance service efficiency and reduce the burden on patients. To address these challenges, healthcare providers should prioritize expanding medication education through accessible resources and dedicated support services for drug-related issues. Policymakers, administrators, and development partners must collaborate to strengthen pharmaceutical services, ensuring equitable access to high-quality HIV care. Future research should explore long-term satisfaction trends, evaluate the effectiveness of digital health tools, and conduct comparative studies across healthcare settings to inform targeted interventions and policies. These efforts are essential for improving health outcomes, preventing drug resistance, and advancing progress toward the UNAIDS 95-95-95 targets in post-war Tigray. By addressing systemic gaps and fostering patient-centered care, stakeholders can rebuild trust in the healthcare system and support the region’s recovery. Declarations Authors’ Contribution Hafte Kahsay Kebede: Conceptualization, Methodology, Formal Analysis, Project Administration, Software, Formal Analysis, Writing Original Draft, Writing Review and Editing. Paul Ward: Conceptualization, Methodology, Validation, Writing – Review and Editing. Hailay Abrha Gesesew: Conceptualization, Methodology, Validation, Writing – Review and Editing. Francesco Checchi: Methodology, Validation. Lillian Mwanri: Validation, Writing – Review and Editing. Mengistu Welday Gebremichael: Conceptualization, Methodology, Validation. Fisseha Ashebir Gebregizabher: Conceptualization, Methodology, Validation. 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International Journal of Public Health Science (IJPHS) 2017;6:7-12. doi: 10.11591/.v6i1.6526 Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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11:17:43\",\"extension\":\"xml\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":320091,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"NCOMMS25236240structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6325865/v1/ac92afa618591a1d2664dcb8.xml\"},{\"id\":94851613,\"identity\":\"a5842c6b-52b8-4450-88c0-fe03351cd329\",\"added_by\":\"auto\",\"created_at\":\"2025-10-31 11:17:43\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":81120,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eConceptual Framework for Pharmaceutical Service Satisfaction Among People Living with HIV (PLWH) in Mekelle City HIV Care Facilities: Integrated Theory of Planned Behavior and Donabedian Model (Tigray, 2024)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e*Key: Solid black lines = Modeled pathways (β coefficients shown); Red lines = Theorized but untested relationships*\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6325865/v1/9f2eba0fe0eaf2234b0c3468.png\"},{\"id\":94851609,\"identity\":\"06b8cb5e-cca6-4ff6-aa07-a504d99d0d79\",\"added_by\":\"auto\",\"created_at\":\"2025-10-31 11:17:43\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":31201,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eConfirmatory Factor Analysis Model of Latent Constructs predicting Satisfaction with Pharmaceutical Services Among People Living with HIV (PLWH) in Mekelle City, 2024.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eP_C:\\u003c/strong\\u003e Provider Communication; \\u003cstrong\\u003eC_R:\\u003c/strong\\u003e Commitment \\u0026amp; Respect; \\u003cstrong\\u003eM_I:\\u003c/strong\\u003eMedication Use Information; \\u003cstrong\\u003eS_D:\\u003c/strong\\u003e Solving Drug Problems\\u003cstrong\\u003e; P_E:\\u003c/strong\\u003ePharmacy Environment\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6325865/v1/f425a555c05c0a6463189437.png\"},{\"id\":94851612,\"identity\":\"94623f5e-4434-4df7-8d28-aa4be0655642\",\"added_by\":\"auto\",\"created_at\":\"2025-10-31 11:17:43\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":383151,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eStructural Equation Model of Satisfaction with Pharmaceutical Services Among People Living with HIV (PLWH) in Mekelle City, Tigray (2024).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eP_C:\\u003c/strong\\u003e Provider Communication; \\u003cstrong\\u003eC_R:\\u003c/strong\\u003e Commitment \\u0026amp; Respect; \\u003cstrong\\u003eM_I:\\u003c/strong\\u003e Medication Use Information; \\u003cstrong\\u003eS_D:\\u003c/strong\\u003e Solving Drug Problems; \\u003cstrong\\u003eP_E:\\u003c/strong\\u003e Pharmacy Environment; \\u003cstrong\\u003eM_A:\\u003c/strong\\u003e Managing Authority; \\u003cstrong\\u003eT_D:\\u003c/strong\\u003eTreatment Duration; \\u003cstrong\\u003eA_I:\\u003c/strong\\u003e Appointment Interval; \\u003cstrong\\u003eGnd:\\u003c/strong\\u003e Gender\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6325865/v1/4bef1a637aea34c0d90ec8bd.png\"},{\"id\":94851619,\"identity\":\"850e9395-f1f0-43a3-8a42-a104969a58cd\",\"added_by\":\"auto\",\"created_at\":\"2025-10-31 11:17:43\",\"extension\":\"jpeg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":152004,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eForest Plot of Odds Ratios for Overall Satisfaction Score with Predictors\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6325865/v1/cffdd2c56fb727eeda7e70bb.jpeg\"},{\"id\":94990410,\"identity\":\"51c428ac-65c0-4944-89e0-5c8997d5cb6a\",\"added_by\":\"auto\",\"created_at\":\"2025-11-03 07:16:56\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1597928,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6325865/v1/29b97161-ff23-4932-adde-86a38c89de3c.pdf\"}],\"financialInterests\":\"There is \\u003cb\\u003eNO\\u003c/b\\u003e Competing Interest.\",\"formattedTitle\":\"Perceived quality of pharmaceutical HIV services in a post-conflict setting: structural equation modelling in Tigray, Ethiopia\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eSub-Saharan Africa (SSA), bearing 70% of global HIV (human immunodeficiency virus) cases\\u003csup\\u003e1\\u003c/sup\\u003e, faces compounded healthcare challenges in conflict-affected regions where armed violence persistently disrupts antiretroviral therapy (ART) services. Recent evidence from Ethiopia\\u003csup\\u003e2\\u003c/sup\\u003e, Sudan\\u003csup\\u003e3\\u003c/sup\\u003e, South Sudan\\u003csup\\u003e4\\u003c/sup\\u003e, and the Democratic Republic of Congo (DRC)\\u003csup\\u003e5\\u003c/sup\\u003e demonstrates that military conflicts collapse pharmaceutical supply chains, destroy health infrastructure, and displace populations \\u0026ndash; resulting in people exceeding 80% without access to health care services, with fewer than 20% of individuals receiving ART. These systemic failures directly undermine the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets, manifesting clinically as increased viral loads \\u003csup\\u003e6-8\\u003c/sup\\u003e, higher mortality rate\\u003csup\\u003e9\\u003c/sup\\u003e, HIV drug resistance\\u003csup\\u003e10\\u003c/sup\\u003e, and secondary HIV transmission through survival-related risk behaviors\\u003csup\\u003e11\\u003c/sup\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe 2020\\u0026ndash;2022 Tigray war serves as a stark case study of healthcare devastation. During the war, 80% of health facilities were destroyed, leading to severe shortages of ART, with availability dropping by 68.8%. ART follow-up rates plummeted to 16.6%, and laboratory services for HIV/AIDS patients declined by 95.5%, affecting approximately 43,000 individuals\\u003csup\\u003e2 12 13\\u003c/sup\\u003e. Post-war recovery phases present unique challenges, as seen in Syria, Yemen, and South Sudan\\u003csup\\u003e4 14 15\\u003c/sup\\u003e, often hindered by destroyed infrastructure, healthcare worker shortages, and supply chain disruptions, keeping ART coverage below 20%\\u003csup\\u003e16-18\\u003c/sup\\u003e. In Tigray, post-war assessments revealed that 62% of people living with HIV (PLWH) faced ART interruptions lasting three months or longer, contributing to rising Non-nucleoside reverse transcriptase inhibitors (NNRTIs) resistance and an 80% decline in clinical follow-up\\u003csup\\u003e19\\u003c/sup\\u003e. HIV prevalence doubled to 3%\\u003csup\\u003e20\\u003c/sup\\u003e, while only 3% of health facilities had fully resumed operations by 2023\\u003csup\\u003e21\\u003c/sup\\u003e, worsening the public health crisis. These findings underscore the urgent need for health system reconstruction and quality assessments of HIV services. In post-conflict Tigray\\u0026apos;s fragile healthcare landscape, evaluating patient experiences with HIV care services becomes critical for rebuilding HIV care systems that address both clinical and psychosocial dimensions of post-conflict recovery.\\u003c/p\\u003e\\n\\u003cp\\u003eThe post-conflict reconstruction of Tigray\\u0026rsquo;s HIV care system demands patient-centered frameworks where satisfaction metrics critically mediate the relationship between service quality and clinical outcomes \\u003csup\\u003e22\\u003c/sup\\u003e. Evidence demonstrates satisfaction\\u0026apos;s multidimensional impact: \\u0026nbsp;provider-patient dynamics significantly influence outcomes: strong relationships increase care reengagement by 55%\\u003csup\\u003e23\\u003c/sup\\u003e, while each additional minute of consultation time raises satisfaction by 0.077 standard deviations (\\u0026beta;=0.077)\\u003csup\\u003e24\\u003c/sup\\u003e. Trust in providers demonstrates a moderate but clinically meaningful effect on adherence (SMD=0.377)\\u003csup\\u003e25\\u003c/sup\\u003e, which directly enables viral suppression (\\u0026lt;50 copies/mL) in 94% of adherent patients\\u003csup\\u003e26\\u003c/sup\\u003e. Adherence thresholds reveal stark outcome disparities: patients maintaining \\u0026ge;95% ART adherence experience 22% treatment failure rates, versus 80% failure among those with \\u0026lt;80% adherence (P\\u0026lt;0.001), alongside 79% fewer hospitalization days (2.6 vs. 12.9 days/1000 follow-up; P=0.001) and elimination of opportunistic infection-related mortality\\u003csup\\u003e27\\u003c/sup\\u003e. Pharmaceutical services are pivotal in this cascade, reducing drug-related problems (DRPs) by 19% per patient (5.2 \\u0026rarr; 4.2; P=0.043) and boosting CD4+ counts by 51.3 cells/mm\\u0026sup3; (P=0.015) through optimized therapy\\u003csup\\u003e28\\u003c/sup\\u003e. Pharmacist-led interventions systematically enhance outcomes, doubling adherence odds (OR=2.70) and quadrupling viral suppression rates (OR=4.13) \\u003csup\\u003e29\\u003c/sup\\u003e, while generating $51\\u0026ndash;$166 annual savings per patient with a 2.51:1 benefit-cost ratio)\\u003csup\\u003e30\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eDRPs account for 59.9% of treatment failures (5.77 vs. 4.08 DRPs/patient in success cohorts)\\u003csup\\u003e31\\u003c/sup\\u003e, underscoring the necessity of integrated pharmaceutical care to sustain virologic control\\u003csup\\u003e27-29 32\\u003c/sup\\u003e. However, post-war Tigray lacks data on patient experiences with pharmaceutical services\\u0026mdash;a critical gap that impedes targeted interventions. Addressing this through satisfaction metrics could identify modifiable barriers (e.g., communication improvements shown to raise adherence odds by 62% (OR=1.62)\\u003csup\\u003e33\\u003c/sup\\u003e, restore healthcare trust, and align reconstruction efforts with patient needs for long-term epidemic control. This study assessed HIV care clients\\u0026rsquo; satisfaction with pharmaceutical services in ART clinics in post-war Mekelle City, Tigray, examining structural, procedural, and outcome-related care dimensions. Findings from the study provided actionable insights to improve ART adherence, retention, and treatment outcomes, offering context-specific recommendations for strengthening pharmaceutical services for people living with HIV (PLHIV) in Tigray and similar conflict-affected settings.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eStudy Design and setting\\u003c/p\\u003e\\n\\u003cp\\u003eThis is an institution-based cross-sectional study conducted among ART patients at 11 health facilities (8 public and 3 non-governmental organization (NGO) managed) in Mekelle City, Tigray. These facilities offer comprehensive HIV services, including clinical evaluation, counseling, antiretroviral drugs, prophylaxis, management of opportunistic infections, and regular follow-up. All medications and services are fully funded by The U.S. President\\u0026apos;s Emergency Plan for AIDS Relief (PEPFAR) provided free of charge to patients\\u003csup\\u003e34 35\\u003c/sup\\u003e. Data collection took place from October 1 to 30, 2024.\\u003c/p\\u003e\\n\\u003cp id=\\\"_Toc188369604\\\"\\u003eSource and Eligible Population\\u003c/p\\u003e\\n\\u003cp\\u003eThe study population comprised adult people living with HIV in Mekelle City undergoing ART follow-ups. Eligible participants were individuals aged 18 or older who had been on ART for at least 12 months at selected health facilities as of October 2024, could understand Tigrigna, and consented to participate. Exclusion criteria included patients who did not consent, were critically ill, had received ART for less than a year, had uncontrolled psychiatric disorders, or were seeking emergency medical care.\\u003c/p\\u003e\\n\\u003cp id=\\\"_Toc188369605\\\"\\u003eSampling\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 24 government and NGO managed facilities providing HIV services in Mekelle City were identified. Using the United States Agency for International Development (USAID) /Deliver Project\\u0026rsquo;s Logistic Indicators Assessment Tool (LIAT)\\u003csup\\u003e36\\u003c/sup\\u003e,\\u0026nbsp;which recommends selecting at least 15% of facilities, 11 facilities (45.83%) were purposively selected based on patient load, facility level, service provision, and agreement to participate. These included 2 medium clinics, 4 health centers, 2 primary hospitals, 2 general hospitals, and 1 referral hospital, with one primary hospital and two clinics managed by NGOs. A sample size of 631 study participants was calculated using a single proportion formula assuming 95% confidence level, 5% margin of error, and 5% non-response rate\\u003csup\\u003e37\\u003c/sup\\u003e. Participants were recruited through consecutive sampling across selected facilities to assess satisfaction with HIV care services in post-conflict Mekelle City.\\u003c/p\\u003e\\n\\u003cp\\u003eThe Conceptual Model\\u003c/p\\u003e\\n\\u003cp\\u003eThis study applies Donabedian\\u0026rsquo;s healthcare quality framework\\u003csup\\u003e38\\u003c/sup\\u003e to examine the relationship between pharmaceutical care quality domains and overall satisfaction with pharmaceutical services. The model categorizes quality into structure (pharmacy environment), process (provider communication, commitment and respect, medication use information, and solving drug problems), and outcome (overall satisfaction). The framework proposed that \\u0026nbsp;structural factors influence process elements, which in turn affect the outcome i.e. patient satisfaction. While Donabedian\\u0026rsquo;s model provides a comprehensive framework, it overlooks potential antecedents of patient satisfaction, as noted by Coyle et al.\\u003csup\\u003e39\\u003c/sup\\u003e. To address this limitation, the study incorporates patient characteristics as a secondary determinant, integrating insights from the value expectancy model and multiple models\\u0026rsquo; theory\\u003csup\\u003e40 41\\u003c/sup\\u003e, which emphasize the role of patient expectations, social and cultural factors, and health status. This adapted model, tested through 12 hypotheses (H1-H12), offers a holistic approach to understanding and improving pharmaceutical service quality\\u003csup\\u003e38 41 42\\u003c/sup\\u003e. Figure 1 presents the proposed model, grounded in this theoretical framework and relevant literature.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ch3\\u003eHypotheses:\\u003c/h3\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eH1:\\u003c/strong\\u003e The pharmacy environment significantly affects provider client communication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eH2:\\u003c/strong\\u003e The pharmacy environment significantly affects provider attitude.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eH3:\\u003c/strong\\u003e The pharmacy environment significantly affects provision of medication information.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eH4:\\u003c/strong\\u003e The pharmacy environment significantly affects solving medication related problems.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eH5:\\u003c/strong\\u003e Satisfaction among people living with HIV is positively correlated with their perception of the quality of provider-client communication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eH6:\\u003c/strong\\u003e Satisfaction among people living with HIV is positively linked to their perceived commitment and respect shown by healthcare providers\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eH7:\\u003c/strong\\u003e Satisfaction among people living with HIV is positively associated with their perceived quality and \\u0026nbsp;the adequacy of medication use information provided\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eH8:\\u003c/strong\\u003e Satisfaction among people living with HIV is positively linked to their perceived quality of providers\\u0026apos; ability to address and resolve drug-related issues.\\u003c/p\\u003e\\n\\u003ch3\\u003eMediating Effects\\u003c/h3\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eH9:\\u003c/strong\\u003e Health facility type moderates the relationship between the pharmacy environment and provider-client communication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eH10:\\u003c/strong\\u003e Health facility type moderates the relationship between the pharmacy environment and provider attitude.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eH11:\\u003c/strong\\u003e Health facility type moderates the relationship between the pharmacy environment and the provision of medication information.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eH12:\\u003c/strong\\u003e Health facility type moderates the relationship between the pharmacy environment and drug-related problem-solving\\u003c/p\\u003e\\n\\u003ch2\\u003eData Collection Instruments\\u003c/h2\\u003e\\n\\u003cp\\u003eTo ensure contextual relevance, we conducted a comprehensive review of studies evaluating HIV care and pharmaceutical service quality dimensions in Ethiopia and sub-Saharan Africa\\u003csup\\u003e43-50\\u003c/sup\\u003e. The survey tools, initially developed in English, were rigorously translated into Tigrigna and back-translated for consistency. A two-phase field test involving 66 HIV-positive ART patients across 11 facilities assessed content validity and reliability. This process, which excluded participants from the main study to maintain data integrity, helped refine the questionnaire\\u0026apos;s language clarity and flow. The study instrument incorporated measures of sociodemographic characteristics and facility-related factors, adapted from the Ethiopia Demographic and Health Survey (DHS), alongside satisfaction predictors.\\u003c/p\\u003e\\n\\u003cp id=\\\"_Toc188369606\\\"\\u003eData Collection\\u003c/p\\u003e\\n\\u003cp\\u003eTo ensure data quality, the questionnaire was encoded and prepared using Qualtrics XM software. Data collectors used smartphones or tablets to conduct exit interviews immediately after patients completed their clinical encounters. Each interview lasted 20\\u0026ndash;30 minutes and were recorded online through the software provided by Torrens University Australia. Eleven trained HIV care providers conducted the interviews. Training was delivered for two days two covering study objectives, data collection procedures, use of the Qualtrics XM software, and participant interview techniques. Data collector gathered facility-specific details from administrators, including facility type, services offered, managing authority, and functional status. To ensure ethical compliance, patients were informed about the study\\u0026apos;s purpose before voluntarily participating in the survey.\\u003c/p\\u003e\\n\\u003cp\\u003eMeasurement of Variables\\u003c/p\\u003e\\n\\u003cp\\u003eThe study assessed perceived HIV care through study participants\\u0026apos; overall satisfaction with pharmaceutical services using 31 items across five key service quality dimensions: (1) provider communication, (2) commitment and respect by the provider, (3) provision of medication use information, (4) solving drug-related problems, and (5) pharmacy environment. Overall satisfaction was evaluated by categorizing responses as \\u0026apos;Satisfied\\u0026apos; or \\u0026apos;Dissatisfied\\u0026apos; based on the mean score. Clients rated their perceptions of service aspects on a 5-point Likert scale, ranging from 1 (extremely dissatisfied) to 5 (extremely satisfied). Supplementary file 5 summarizes the indicators used to construct the quality scales, providing a comparative basis for assessing service quality dimensions in the study setting.\\u003c/p\\u003e\\n\\u003cp\\u003eOperational Definitions\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePatient satisfactions:\\u0026nbsp;\\u003c/strong\\u003eRefers to an individual \\u0026nbsp;study participants\\u0026rsquo; evaluation of the pharmaceutical care they receive in a health-care setting based on their experiences in their treatment journey.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHIV Care Clients Experience:\\u0026nbsp;\\u003c/strong\\u003eHIV care client experience refers to the interactions between HIV care services and the people living with HIV over at least 12 months. These interactions include dispensing, counseling, monitoring, and addressing medication-related issues.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLevel of Satisfaction:\\u003c/strong\\u003e Based on the composite mean of the five points Likert scaled items analysis result, a mean score of \\u0026gt; 3.57 was classified as \\u0026ldquo;Satisfied\\u0026rdquo; while those with mean score \\u003cu\\u003e\\u0026lt;\\u003c/u\\u003e\\u0026nbsp; 3.57 were classified as \\u0026ldquo;Dissatisfied\\u0026rdquo;.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eOther Measures:\\u0026nbsp;\\u003c/strong\\u003eStudy participants self-reported their gender, age, marital status, education level, income, and family size. Satisfaction indicators were assessed using a validated question: \\u0026ldquo;In general, over the past 24 months, how satisfied are you with (service)?\\u0026rdquo; Responses were recorded on a 5-point Likert scale, ranging from \\u0026ldquo;Extremely Dissatisfied\\u0026rdquo; to \\u0026ldquo;Extremely Satisfied.\\u0026rdquo;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eData Analysis\\u003c/p\\u003e\\n\\u003cp\\u003eSatisfaction is a complex, multidimensional construct. To address this complexity, this study employed the Donabedian Framework\\u003csup\\u003e38\\u003c/sup\\u003e, and structural equation modeling (SEM) incorporating facility characteristics, socio-demographic attributes, and clinical factors. \\u0026nbsp;A two-step SEM approach was used, starting with a measurement model to identify latent variables from observed indicators.\\u003c/p\\u003e\\n\\u003cp\\u003eData preprocessing involved listwise exclusion of incomplete responses (n = 20) and participants exhibiting insufficient variability (composite score range: 0.5\\u0026ndash;4.5; SD \\u0026ge;0.25 threshold) (n = 50) to mitigate response bias. Categorical predictors underwent factorization with reference category specification, while nominal variables were orthogonal dummy-coded (k-1 contrasts). Normality assumptions were evaluated through quantile-quantile deviation plots, complemented by Shapiro-Wilk (W \\u0026gt;0.95, P \\u0026gt;0.05) and Kolmogorov-Smirnov (D \\u0026lt;0.05, \\u0026alpha;=0.05) tests, with distributional metrics (skewness |\\u0026lt;2|, kurtosis |\\u0026lt;7|) confirming parametric conditions.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe measurement model underwent confirmatory factor analysis (CFA) using a diagonally weighted least squares (DWLS) estimator, appropriate for ordinal/non-normal data. Seven items exhibiting psychometric inadequacies, including high cross-loadings (PC1, CR1) and suboptimal factor loadings (\\u0026lt;0.6: PC3, MI6, MT4, MA1, IA4), were iteratively removed to achieve model parsimony (see supplementary file 2). Scale reliability met established benchmarks (Cronbach\\u0026rsquo;s \\u0026alpha; \\u0026ge;0.7), with convergent validity demonstrated through standardized regression weights (\\u0026ge;0.50), composite reliability (CR \\u0026ge;0.7), and average variance extracted (AVE \\u0026ge;0.5). Discriminant validity adhered to the Fornell-Larcker criterion, where AVE values exceeded squared inter-construct correlations (\\u0026le;0.80). Prior to structural equation modeling, multicollinearity diagnostics identified three exogenous variables requiring exclusion due to excessive collinearity (VIF \\u0026gt;10: Facility Type (55.18), Level of Care (13.55), Service Type (18.01)).\\u003c/p\\u003e\\n\\u003cp\\u003eThe structural equation model quantified associations between service quality dimensions and overall satisfaction using standardized correlation coefficients (\\u0026beta;). Direct, indirect, and total effects were computed to capture unmediated influences and mediation pathways. Path diagrams visualized relationships among five first-order constructs (provider communication, commitment \\u0026amp; respect, medication use information, solving drug problems, and pharmacy environment) and the second-order construct of overall satisfaction. Model fit was assessed using multiple indices (WRMR \\u0026le;1.0, CFI \\u0026gt;0.90, TLI \\u0026gt;0.90, RMSEA \\u0026le;0.08) \\u003csup\\u003e51 52\\u003c/sup\\u003e, excluding \\u0026chi;\\u0026sup2; due to sample size sensitivity. Diagnostic checks, including multicollinearity tests and reliability assessments, ensured robustness. The Diagonally Weighted Least Squares (DWLS) estimator was employed to handle non-normal data, facilitating proper identification of key predictors in the model.\\u003c/p\\u003e\\n\\u003cp\\u003eThe analysis of overall satisfaction used a composite score approach, calculating the mean of individual satisfaction indicators for each respondent across a total sample of 526 participants. These composite scores were categorized into \\u0026quot;Satisfied\\u0026quot; and \\u0026quot;Dissatisfied\\u0026quot; groups based on the sample\\u0026apos;s overall mean score (3.57), creating a binary outcome variable. Due to the non-normal distribution of the data, binary logistic regression was applied using generalized linear models (GLMs) to assess the effects of sociodemographic predictors on satisfaction. To improve robustness and account for participant-level variability, generalized linear mixed-effects models (GLMER) were also employed, incorporating random effects. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to measure the strength and significance of predictor effects. All analyses were performed using R (version 4.3.1).\\u003c/p\\u003e\\n\\u003cp\\u003eEthical Consideration\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eEthical approval for this study was obtained from the Institutional Review Board of Tigray Health Research Institute(THRI) (THRI/4031/0502/16, 7 February 2024) and Torrens University Australia Human Research Ethics Committee(TUA-HREC) (Ethics Application 0333, 14 May 2024). The Tigray Region Health Bureau(TRHB) granted permission (Ref No. 2579/365/16, 12 March 2024). Participants were informed about the study\\u0026apos;s objectives, assured of confidentiality and their right to withdraw without consequences. Written or documented oral informed consent was obtained from all participants.\\u003c/p\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003ch2\\u003eOverview of the Study Population\\u003c/h2\\u003e\\n\\u003cp\\u003eThe study recruited 631 participants, of whom 596 completed the survey, yielding a 94.4% response rate. After eligibility screening, 526 respondents (88.3% of those who responded) were retained for final analysis. The sample predominantly comprised middle-aged adults (66.5%), followed by older adults (24.3%). Females constituted the majority (60.6%) of participants, and 53.6% were married. Regarding education, most participants had completed primary school (38.2%), followed by those with secondary education (29.3%). In terms of employment, the largest group was self-employed (29.8%), closely followed by those employed in government or private sectors (27.6%). (supplementary file 1).\\u003c/p\\u003e\\n\\u003ch2\\u003eConfirmatory Factor Analysis (CFA) outputs\\u0026nbsp;\\u003c/h2\\u003e\\n\\u003cp\\u003eThe confirmatory factor analysis (CFA) employing robust maximum likelihood estimation validated a theoretically derived measurement model comprising five latent constructs: provider communication, commitment and respect, medication use information, solving drug problems, and pharmacy environment. Global fit indices demonstrated exceptional alignment with the hypothesized structure comparative fit index (CFI) = 0.996; Tucker\\u0026minus;Lewis Index (TLI) = 0.995, surpassing the 0.95 threshold for excellent fit. Residual analysis further supported model adequacy standardized root mean square residual (SRMR)=0.052, with approximate fit metrics root mean square error of approximation (RMSEA) = 0.080;90 indicating acceptable population-level discrepancy.\\u003c/p\\u003e\\n\\u003cp\\u003eAll constructs exhibited strong psychometric properties, with standardized factor loadings exceeding 0.70 across indicators (p \\u0026lt; 0.001), demonstrating statistically significant and substantively meaningful relationships between observed variables and their respective latent factors. Specific loading ranges included provider communication (0.819\\u0026ndash;0.933), commitment \\u0026amp; respect (0.883\\u0026ndash;0.923), medication use information (0.806\\u0026ndash;0.886), solving drug problems (0.704\\u0026ndash;0.927), and pharmacy environment (0.821\\u0026ndash;0.935), collectively explaining 58\\u0026ndash;87% of item variance (R\\u0026sup2; = 0.58\\u0026ndash;0.87) (supplementary file 3). Internal consistency reliability, assessed via Cronbach\\u0026rsquo;s \\u0026alpha;, exceeded the 0.70 benchmark for all scales (\\u0026alpha; = 0.88\\u0026ndash;0.91), with medication use information demonstrating optimal reliability (\\u0026alpha; = 0.91). Item retention was further justified through corrected item-total correlations (\\u0026gt;0.50) and invariance testing confirming stability across model iterations. The CFA path diagram is plotted; see Figure 2 for details.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eInter-factor correlations revealed theoretically coherent relationships, most notably between provider communication and commitment \\u0026amp; respect (\\u0026phi; = 0.650, p \\u0026lt; 0.001), while pharmacy environment showed weaker yet significant associations with other constructs (\\u0026phi; = 0.250\\u0026ndash;0.400). Critical covariances included provider communication \\u0026harr; solving drug problems (\\u0026psi; = 0.917, p \\u0026lt; 0.001) and medication use information \\u0026harr; solving drug problems (\\u0026psi; = 0.897, p \\u0026lt; 0.001), suggesting synergistic operationalization of these dimensions in shaping pharmaceutical service satisfaction. (See Table 1\\u0026amp;2)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1: Covariances Among Latent Variables\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 51.4423%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLatent Variables\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.141%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEstimate\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 20.9936%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCovariance (R)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14.4231%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 51.4423%;\\\"\\u003e\\n \\u003cp\\u003eProvider Communication \\u0026harr; Commitment \\u0026amp; Respect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.141%;\\\"\\u003e\\n \\u003cp\\u003e0.639\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 20.9936%;\\\"\\u003e\\n \\u003cp\\u003e0.852\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14.4231%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 51.4423%;\\\"\\u003e\\n \\u003cp\\u003eProvider Communication \\u0026harr; Medication Use Info\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.141%;\\\"\\u003e\\n \\u003cp\\u003e0.614\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 20.9936%;\\\"\\u003e\\n \\u003cp\\u003e0.900\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14.4231%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 51.4423%;\\\"\\u003e\\n \\u003cp\\u003eProvider Communication \\u0026harr; Solving Drug Problems\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.141%;\\\"\\u003e\\n \\u003cp\\u003e0.695\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 20.9936%;\\\"\\u003e\\n \\u003cp\\u003e0.917\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14.4231%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 51.4423%;\\\"\\u003e\\n \\u003cp\\u003eProvider Communication \\u0026harr; Pharmacy Environment\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.141%;\\\"\\u003e\\n \\u003cp\\u003e0.547\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 20.9936%;\\\"\\u003e\\n \\u003cp\\u003e0.786\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14.4231%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 51.4423%;\\\"\\u003e\\n \\u003cp\\u003eCommitment \\u0026amp; Respect \\u0026harr; Medication Use Info\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.141%;\\\"\\u003e\\n \\u003cp\\u003e0.574\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 20.9936%;\\\"\\u003e\\n \\u003cp\\u003e0.753\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14.4231%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 51.4423%;\\\"\\u003e\\n \\u003cp\\u003eCommitment \\u0026amp; Respect \\u0026harr; Solving Drug Problems\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.141%;\\\"\\u003e\\n \\u003cp\\u003e0.660\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 20.9936%;\\\"\\u003e\\n \\u003cp\\u003e0.779\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14.4231%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 51.4423%;\\\"\\u003e\\n \\u003cp\\u003eCommitment \\u0026amp; Respect \\u0026harr; Pharmacy Environment\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.141%;\\\"\\u003e\\n \\u003cp\\u003e0.650\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 20.9936%;\\\"\\u003e\\n \\u003cp\\u003e0.835\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14.4231%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 51.4423%;\\\"\\u003e\\n \\u003cp\\u003eMedication Use Info \\u0026harr; Solving Drug Problems\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.141%;\\\"\\u003e\\n \\u003cp\\u003e0.691\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 20.9936%;\\\"\\u003e\\n \\u003cp\\u003e0.897\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14.4231%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 51.4423%;\\\"\\u003e\\n \\u003cp\\u003eMedication Use Info \\u0026harr; Pharmacy Environment\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.141%;\\\"\\u003e\\n \\u003cp\\u003e0.470\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 20.9936%;\\\"\\u003e\\n \\u003cp\\u003e0.664\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14.4231%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 51.4423%;\\\"\\u003e\\n \\u003cp\\u003eSolving Drug Problems \\u0026harr; Pharmacy Environment\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.141%;\\\"\\u003e\\n \\u003cp\\u003e0.562\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 20.9936%;\\\"\\u003e\\n \\u003cp\\u003e0.715\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14.4231%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2: Latent Variable Correlations\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"648\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 34.2593%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLatent Variable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 19.4444%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eProvider Communication\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15.7407%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCommitment and Respect\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 16.6667%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMedication Use Information\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.8889%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSolving Drug Problems\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 34.2593%;\\\"\\u003e\\n \\u003cp\\u003eCommitment and Respect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 19.4444%;\\\"\\u003e\\n \\u003cp\\u003e0.852\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15.7407%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 16.6667%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.8889%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 34.2593%;\\\"\\u003e\\n \\u003cp\\u003eMedication Use Information\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 19.4444%;\\\"\\u003e\\n \\u003cp\\u003e0.900\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15.7407%;\\\"\\u003e\\n \\u003cp\\u003e0.753\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 16.6667%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.8889%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 34.2593%;\\\"\\u003e\\n \\u003cp\\u003eSolving Drug Problems\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 19.4444%;\\\"\\u003e\\n \\u003cp\\u003e0.917\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15.7407%;\\\"\\u003e\\n \\u003cp\\u003e0.779\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 16.6667%;\\\"\\u003e\\n \\u003cp\\u003e0.897\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.8889%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 34.2593%;\\\"\\u003e\\n \\u003cp\\u003ePharmacy Environment\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 19.4444%;\\\"\\u003e\\n \\u003cp\\u003e0.786\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15.7407%;\\\"\\u003e\\n \\u003cp\\u003e0.835\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 16.6667%;\\\"\\u003e\\n \\u003cp\\u003e0.664\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13.8889%;\\\"\\u003e\\n \\u003cp\\u003e0.715\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003ch2\\u003eStructural Equation Modeling (SEM) outputs\\u0026nbsp;\\u003c/h2\\u003e\\n\\u003cp\\u003eThe second-order structural equation model, analyzed using the Diagonally Weighted Least Squares (DWLS) estimator to accommodate non-normal, ordinal data, demonstrated excellent fit to the observed data. Fit indices strongly supported the model\\u0026apos;s adequacy: Comparative Fit Index (CFI) = 0.992, Tucker-Lewis Index (TLI) = 0.991, Root Mean Square Error of Approximation (RMSEA) = 0.032 (90% CI: 0.026, 0.037), and Standardized Root Mean Square Residual (SRMR) = 0.060. While the \\u0026chi;\\u0026sup2; test was significant (p\\u0026lt;0.001), this is expected given the large sample size (N=526) and does not necessarily indicate poor fit.\\u003c/p\\u003e\\n\\u003cp\\u003eThe model exhibited robust psychometric properties, with all factor loadings exceeding 0.60 and achieving statistical significance (p \\u0026lt; 0.001), ensuring strong convergent validity and reliability. Low residual variances further corroborated the model\\u0026apos;s strength, indicating that observed variables effectively captured their respective latent constructs. The latent variables, provider communication, commitment and respect, medication use information, solving drug problems, pharmacy environment, and overall satisfaction, all demonstrated statistically significant relationships with their observed indicators (p \\u0026lt; 0.001). Notably, provider communication showed strong associations with its indicators (PC2, PC4, PC5, PC6), with standardized loadings ranging from 0.683 to 0.869, while commitment and respect exhibited loadings ranging from 0.761 to 0.865 for its indicators (CR2, CR3, CR4, CR5). (see supplementary file 4)\\u003c/p\\u003e\\n\\u003ch3\\u003eStructural Relationships and Regression Coefficients\\u003c/h3\\u003e\\n\\u003cp\\u003eThe structural equation modeling analysis revealed significant positive effects of the pharmacy environment on various dimensions of pharmaceutical service provision in ART clinics in Mekelle City. The pharmacy environment showed a stronger association with commitment \\u0026amp; respect (\\u0026beta; = 0.868, p \\u0026lt; .001) than with provider communication (\\u0026beta; = 0.783, p \\u0026lt; .001), exceeding Cohen\\u0026apos;s threshold for large effect sizes (\\u0026beta; \\u0026gt; 0.50) in behavioral research. These path coefficients suggest the pharmacy environment explains approximately 75.3% of variance in commitment \\u0026amp; respect (R\\u0026sup2; = 0.868\\u0026sup2;) and 61.3% in provider communication (R\\u0026sup2; = 0.783\\u0026sup2;), reflecting its critical role in shaping interpersonal care dimensions. The positive directional relationships imply that each standard deviation improvement in environmental factors corresponds to 0.868 SD and 0.783 SD increases in commitment \\u0026amp; respect and provider communication scores respectively (see table 3).\\u0026nbsp;The SEM path diagram is plotted; see Figure 3 for details.\\u003c/p\\u003e\\n\\u003cp\\u003eThe hierarchical structural equation model positioned overall satisfaction as a second-order latent variable synthesized from four service quality dimensions, with medication use information demonstrating the strongest predictive power (\\u0026beta; = 0.682, p \\u0026lt; .001) implying that each standard deviation improvement in patients\\u0026apos; understanding of medication use amplified satisfaction by 68.2% of a standard deviation. Solving Drug Problems (\\u0026beta; = 0.644, p \\u0026lt; .001) emerged as a critical operational lever, where systematic resolution of medication related problems contributed 64.4% of a SD satisfaction gain. Provider communication (\\u0026beta; = 0.594, p \\u0026lt; .001) highlighted the centrality of pharmacists\\u0026apos; empathetic dialogue and jargon-free explanations, while commitment \\u0026amp; respect (\\u0026beta; = 0.377, p \\u0026lt; .05) represented a foundational yet less influential expectation of professional decorum. Externally, extended appointment intervals (\\u0026beta; = 0.366, p \\u0026lt; .001) correlating with 36.6% higher satisfaction per SD increase in scheduling flexibility, whereas prolonged treatment duration eroded satisfaction by 16.7% per SD (\\u0026beta; = -0.167, p = .011). The model\\u0026apos;s exceptional explanatory capacity (R\\u0026sup2; = 84.4%) was corroborated by first-order construct R\\u0026sup2; values spanning 80.5%-94.4%, with provider communication\\u0026apos;s 94.4% variance explanation (residual \\u0026sigma;\\u0026sup2; = 0.040) and solving drug problems\\u0026apos; 91.4% (\\u0026sigma;\\u0026sup2; = 0.086) indicating near-complete capture of these dimensions\\u0026apos; determinants.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3:\\u003c/strong\\u003e Regression analysis assesses the relationships between latent variables and overall satisfaction, including demographic variables.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54.8077%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eRelationship\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 26.9231%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eStandardized Estimate (\\u0026beta;)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18.2692%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ep-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54.8077%;\\\"\\u003e\\n \\u003cp\\u003ePharmacy Environment \\u0026rarr; Provider Communication\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 26.9231%;\\\"\\u003e\\n \\u003cp\\u003e0.783\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18.2692%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54.8077%;\\\"\\u003e\\n \\u003cp\\u003ePharmacy Environment \\u0026rarr; Commitment \\u0026amp; Respect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 26.9231%;\\\"\\u003e\\n \\u003cp\\u003e0.868\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18.2692%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54.8077%;\\\"\\u003e\\n \\u003cp\\u003ePharmacy Environment \\u0026rarr; Medication Use Information\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 26.9231%;\\\"\\u003e\\n \\u003cp\\u003e0.644\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18.2692%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54.8077%;\\\"\\u003e\\n \\u003cp\\u003ePharmacy Environment \\u0026rarr; Solving Drug Problems\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 26.9231%;\\\"\\u003e\\n \\u003cp\\u003e0.685\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18.2692%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54.8077%;\\\"\\u003e\\n \\u003cp\\u003eManaging Authority \\u0026rarr; Overall Satisfaction\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 26.9231%;\\\"\\u003e\\n \\u003cp\\u003e-0.070\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18.2692%;\\\"\\u003e\\n \\u003cp\\u003e0.280\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54.8077%;\\\"\\u003e\\n \\u003cp\\u003eAppointment Interval \\u0026rarr; Overall Satisfaction\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 26.9231%;\\\"\\u003e\\n \\u003cp\\u003e0.366\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18.2692%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54.8077%;\\\"\\u003e\\n \\u003cp\\u003eTreatment Duration \\u0026rarr; Overall Satisfaction\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 26.9231%;\\\"\\u003e\\n \\u003cp\\u003e-0.167\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18.2692%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54.8077%;\\\"\\u003e\\n \\u003cp\\u003eMarital Status \\u0026rarr; Overall Satisfaction\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 26.9231%;\\\"\\u003e\\n \\u003cp\\u003e-0.119\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18.2692%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54.8077%;\\\"\\u003e\\n \\u003cp\\u003eAge \\u0026rarr; Overall Satisfaction\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 26.9231%;\\\"\\u003e\\n \\u003cp\\u003e0.051\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18.2692%;\\\"\\u003e\\n \\u003cp\\u003e0.033\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54.8077%;\\\"\\u003e\\n \\u003cp\\u003eGender \\u0026rarr; Overall Satisfaction\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 26.9231%;\\\"\\u003e\\n \\u003cp\\u003e0.069\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18.2692%;\\\"\\u003e\\n \\u003cp\\u003e0.310\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003ch2\\u003eBinomial Regression Analysis of Pharmaceutical Service Satisfaction\\u003c/h2\\u003e\\n\\u003cp\\u003eThe binomial regression analysis revealed a polarized satisfaction landscape, with 57.2% of respondents reporting satisfaction with pharmaceutical services against 42.8% dissatisfaction. Service dimensions exhibited marked variability:\\u0026nbsp;commitment \\u0026amp; respect achieved the highest satisfaction (76.4%), attributable to pharmacist availability, privacy adherence, and dignity in interactions, while pharmacy environment followed at 71.5%. Critical deficits emerged in provider communication (58.9% satisfied), medication use information (49%), and solving drug problems (50.2%), with dissatisfaction rooted in insufficient guidance on side effects (reported by 63% of dissatisfied respondents), drug interaction protocols, and missed-dose management strategies.\\u003c/p\\u003e\\n\\u003cp\\u003eThe binomial regression analysis identified facility type as the strongest predictor of pharmaceutical service satisfaction, with health centers demonstrating substantially higher satisfaction levels compared to referral hospitals (\\u0026beta; = 1.281, p \\u0026lt; 0.001). Conversely, primary hospitals showed markedly lower satisfaction (\\u0026beta; = -1.789, p = 0.003), equivalent to an 83% reduction in satisfaction odds relative to referral hospitals. Extended appointment intervals (\\u0026gt;3 months) significantly enhanced satisfaction likelihood (\\u0026beta; = 1.732, p \\u0026lt; 0.001), translating to 5.6-fold greater odds compared to shorter intervals. Geographical disparities emerged with non-Mekelle residents reporting higher satisfaction (\\u0026beta; = 0.794, p = 0.028).\\u003c/p\\u003e\\n\\u003cp\\u003eDemographic factors revealed nuanced patterns: middle-aged (\\u0026beta; = -0.701, p = 0.11) and older adults (\\u0026beta; = -0.844, p = 0.098) showed non-significant trends toward lower satisfaction versus young adults. Housing stability indicators approached significance, with patients living with family/relatives demonstrating marginally reduced satisfaction (\\u0026beta; = -0.785, p = 0.053) compared to homeowners. Clinical history variables showed limited predictive power, as extended ART duration (\\u0026gt;10 years) marginally reduced satisfaction (\\u0026beta; = -0.433, p = 0.077) without achieving statistical significance.\\u003c/p\\u003e\\n\\u003cp\\u003eThe model\\u0026apos;s non-significant intercept (\\u0026beta; = 0.994, p = 0.185) indicates balanced baseline satisfaction across reference categories. Several table-specified predictors, including General Hospital status (\\u0026beta; = -0.133, p = 0.686), rental housing (\\u0026beta; = -0.145, p = 0.587), and Medium Clinic designation (\\u0026beta; = 1.202, p = 0.133), failed to demonstrate statistical significance. (see Figure 4)\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe study highlights a complex interplay of factors influencing people living with HIV satisfaction with pharmaceutical services in a post conflict context using the case of \\u0026nbsp;Tigray. Pharmacies in Ethiopian public health facilities often struggle with high patient volumes, overcrowding, limited consultation spaces, medication shortages, and a lack of electronic record systems, contributing to patient dissatisfaction\\u003csup\\u003e53-55\\u003c/sup\\u003e. In contrast, ART clinics generally offer better-organized services with private consultation rooms, trained staff, and free of charge medications. However, our study found that satisfaction with pharmaceutical services in Mekelle City ART clinics (57.2%) was significantly lower than the satisfaction rates reported from outpatient pharmacies(60.4%\\u0026ndash;65.37%)\\u003csup\\u003e56-58\\u003c/sup\\u003e;\\u0026nbsp;and ART service satisfaction levels in Ethiopia (70.7%\\u0026ndash;86.4%)\\u003csup\\u003e59-61\\u003c/sup\\u003e, and Cameroon (91.2%)\\u003csup\\u003e62\\u003c/sup\\u003e. Pre-war studies in Tigray also reported much higher satisfaction (75.2%\\u0026ndash;89.6%)\\u003csup\\u003e63 64\\u003c/sup\\u003e. Key areas of concern include low satisfaction with provider communication (58.9%), medication use information (49%), and solving drug problems (50.2%), all falling below the national benchmark of \\u0026gt;85%.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe primary reason for high dissatisfaction of \\u0026nbsp;PLWH stems from the prolonged disruption of HIV care during and after the Tigray War. Over 83% of patients were lost to follow-up during the conflict\\u003csup\\u003e12\\u003c/sup\\u003e, with many requiring transitions to second- or third-line ART regimens upon return\\u003csup\\u003e13 65\\u003c/sup\\u003e, which often have more severe side effects and necessitate frequent monitoring and shorter appointment intervals\\u003csup\\u003e66\\u003c/sup\\u003e. The healthcare system remains fragmented, with a significant loss of skilled providers and a surge in patient load, which has doubled from pre-war levels to 3% prevalence\\u003csup\\u003e20\\u003c/sup\\u003e which further hinder providers\\u0026apos; ability to manage drug therapy, regimen changes, side effects, and adherence strategies. Furthermore, many HIV care providers were displaced due to the war\\u003csup\\u003e67\\u003c/sup\\u003e, remaining or newly appointed providers struggle with managing complex regimens, including Dolutegravir-based therapy, due to limited expertise.\\u003c/p\\u003e\\n\\u003cp\\u003eThe study findings highlight that medication use information, solving drug problems, and provider communication are the most influential predictors of HIV care patients\\u0026rsquo; satisfaction with pharmaceutical services. Effective medication counseling, including adherence strategies, side effect management, and regimen education, significantly enhances patient satisfaction and adherence\\u003csup\\u003e68-70\\u003c/sup\\u003e which in turn impacts the HIV care outcomes including virological success and drug resistance\\u003csup\\u003e25-27\\u003c/sup\\u003e. Similarly, resolving issues such as adverse effects, drug interactions, and stockouts is essential for sustaining patient engagement and treatment continuity\\u003csup\\u003e71-73\\u003c/sup\\u003e. These findings underscore the importance of comprehensive pharmaceutical care in improving HIV treatment outcomes.\\u003c/p\\u003e\\n\\u003cp\\u003eThe Pharmacy Environment also plays a pivotal role, indirectly influencing satisfaction by enhancing other dimensions such as Provider Communication, medication use information, and solving drug problems. A well-structured, patient-centered pharmacy setting featuring private consultation areas, efficient workflows, and welcoming designs ensures confidentiality, reduces stigma, and promotes open communication\\u003csup\\u003e57 74-76\\u003c/sup\\u003e. These elements are crucial for fostering trust and improving service experiences, which can lead to better adherence and health outcomes\\u003csup\\u003e77 78\\u003c/sup\\u003e. While commitment and respect had a relatively smaller impact, it remain an important contributor to satisfaction, aligning with prior research that emphasizes patient-centered care as a key determinant of HIV treatment success\\u003csup\\u003e79-81\\u003c/sup\\u003e. Strengthening these areas, in line with Ethiopia\\u0026rsquo;s Compassionate, Respectful, and Caring (CRC) initiative, can improve patient-provider relationships, enhance compliance, and ensure continuity of care\\u003csup\\u003e38 82-84\\u003c/sup\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe study\\u0026apos;s findings have critical implications for HIV care in post-conflict Tigray. Low satisfaction with pharmaceutical services threatens ART adherence, increasing the risk of HIV drug resistance (HIVDR) and potentially reversing decades of progress in HIV care\\u003csup\\u003e85-87\\u003c/sup\\u003e. Disruptions in treatment continuity may lead to ongoing transmission, higher mortality rates, and costly interventions to re-engage lost patients. Additionally, the spread of resistant HIV strains poses a significant challenge to epidemic control efforts, further straining the already fragile healthcare system. Ensuring consistent treatment and patient retention is essential to achieving viral suppression and preventing further public health crises.\\u003c/p\\u003e\"},{\"header\":\"Conclusion and Recommendations\",\"content\":\"\\u003cp\\u003eThe HIV prevalence in post-conflict Tigray has doubled to 3%, underscoring the urgent need to strengthen HIV care services in post-conflict Tigray. This study reveals a mixed picture of patient satisfaction: while clients expressed relative satisfaction with provider-client interactions and pharmacy infrastructure, significant dissatisfaction persists in critical areas such as medication use information and drug-related problem-solving. These gaps, exacerbated by service disruptions and resource constraints, highlight the need for targeted interventions to improve care quality, treatment adherence, and patient well-being. Strengthening pharmacist training, optimizing workflows, and implementing structured refill schedules can enhance service efficiency and reduce the burden on patients.\\u003c/p\\u003e\\n\\u003cp\\u003eTo address these challenges, healthcare providers should prioritize expanding medication education through accessible resources and dedicated support services for drug-related issues. Policymakers, administrators, and development partners must collaborate to strengthen pharmaceutical services, ensuring equitable access to high-quality HIV care. Future research should explore long-term satisfaction trends, evaluate the effectiveness of digital health tools, and conduct comparative studies across healthcare settings to inform targeted interventions and policies. These efforts are essential for improving health outcomes, preventing drug resistance, and advancing progress toward the UNAIDS 95-95-95 targets in post-war Tigray. By addressing systemic gaps and fostering patient-centered care, stakeholders can rebuild trust in the healthcare system and support the region\\u0026rsquo;s recovery.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eAuthors\\u0026rsquo; Contribution\\u003c/h2\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHafte Kahsay Kebede:\\u003c/strong\\u003e Conceptualization, Methodology, Formal Analysis, Project Administration, Software, Formal Analysis, Writing Original Draft, Writing Review and Editing. \\u003cstrong\\u003ePaul Ward:\\u003c/strong\\u003e Conceptualization, Methodology, Validation, Writing \\u0026ndash; Review and Editing. \\u003cstrong\\u003eHailay Abrha Gesesew:\\u003c/strong\\u003e Conceptualization, Methodology, Validation, Writing \\u0026ndash; Review and Editing. \\u003cstrong\\u003eFrancesco Checchi:\\u003c/strong\\u003e Methodology, Validation. \\u003cstrong\\u003eLillian Mwanri:\\u003c/strong\\u003e Validation, Writing \\u0026ndash; Review and Editing. \\u003cstrong\\u003eMengistu Welday Gebremichael:\\u003c/strong\\u003e Conceptualization, Methodology, Validation. \\u003cstrong\\u003eFisseha Ashebir Gebregizabher:\\u003c/strong\\u003e Conceptualization, Methodology, Validation. \\u003cstrong\\u003eGebremeskel Mamu Werede:\\u003c/strong\\u003e Software, Formal Analysis.\\u003c/p\\u003e\\n\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\n\\u003cp\\u003eNo funding.\\u003c/p\\u003e\\n\\u003ch2\\u003eDeclaration of competing interest\\u003c/h2\\u003e\\n\\u003cp\\u003eThe authors have no competing interest to declare.\\u003c/p\\u003e\\n\\u003ch2\\u003eAcknowledgements\\u003c/h2\\u003e\\n\\u003cp\\u003eWe thank the study participants and data collectors.\\u003cbr\\u003e\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eGesesew H, Berhane K, Siraj ES, et al. 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Adverse drug reactions to antiretroviral drugs and impact on treatment adherence among HIV patients in northwestern Nigeria. \\u003cem\\u003eDrugs \\u0026amp; Therapy Perspectives\\u003c/em\\u003e 2018;34(10):488-95. doi: 10.1007/s40267-018-0546-7\\u003c/li\\u003e\\n\\u003cli\\u003eZhang L, Li X, Lin Z, et al. Side effects, adherence self-efficacy, and adherence to antiretroviral treatment: a mediation analysis in a Chinese sample. \\u003cem\\u003eAIDS care\\u003c/em\\u003e 2016;28(7):919-26.\\u003c/li\\u003e\\n\\u003cli\\u003eFonsah JY, Njamnshi AK, Kouanfack C, et al. Adherence to antiretroviral therapy (ART) in Yaound\\u0026eacute;-Cameroon: association with opportunistic infections, depression, ART regimen and side effects. \\u003cem\\u003ePloS one\\u003c/em\\u003e 2017;12(1):e0170893.\\u003c/li\\u003e\\n\\u003cli\\u003eAhmad DM, Abonyi EE, Chukwudi Ugwuonah J, et al. HIV Patients\\u0026rsquo; Satisfaction with Pharmaceutical Care at a Nigerian Tertiary Healthcare Facility During the Covid-19 Pandemic. \\u003cem\\u003eJournal of the International Association of Providers of AIDS Care (JIAPAC)\\u003c/em\\u003e 2023;22:23259582231159093. doi: 10.1177/23259582231159093\\u003c/li\\u003e\\n\\u003cli\\u003eGoldstein D, Salvatore M, Ferris R, et al. Integrating global HIV services with primary health care: a key step in sustainable HIV epidemic control. \\u003cem\\u003eThe Lancet Global Health\\u003c/em\\u003e 2023;11(7):e1120-e24. doi: 10.1016/S2214-109X(23)00156-0\\u003c/li\\u003e\\n\\u003cli\\u003eOrganization WH. Operations Manual for Delivery of HIV Prevention, Care and Treatment at Primary Health Centres in High-Prevalence, Resource-Constrained Settings Edition 1 for Field-testing. 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Patient\\u0026ndash;provider interaction, patient satisfaction, and health outcomes: testing explanatory models for people living with HIV/AIDS. \\u003cem\\u003eAIDS Care\\u003c/em\\u003e 2015;27(8):972-78. doi: 10.1080/09540121.2015.1015478\\u003c/li\\u003e\\n\\u003cli\\u003eKarunamoorthi K, Rajalakshmi M, Babu SM, et al. HIV/AIDS patient\\u0026rsquo;s satisfactory and their expectations with pharmacy service at specialist antiretroviral therapy (ART) units. \\u003cem\\u003eEur Rev Med Pharmacol Sci\\u003c/em\\u003e 2009;13(5):331-39.\\u003c/li\\u003e\\n\\u003cli\\u003eAbebe TB, Erku DA, Gebresillassie BM, et al. Expectation and satisfaction of HIV/AIDS patients toward the pharmaceutical care provided at Gondar University Referral Hospital, Northwestern Ethiopia: a cross-sectional study. \\u003cem\\u003ePatient Preference and Adherence\\u003c/em\\u003e 2016;10(null):2073-82. doi: 10.2147/PPA.S114720\\u003c/li\\u003e\\n\\u003cli\\u003eBatbaatar E, Dorjdagva J, Luvsannyam A, et al. Determinants of patient satisfaction: a systematic review. \\u003cem\\u003ePerspect Public Health\\u003c/em\\u003e 2017;137(2):89-101. doi: 10.1177/1757913916634136 [published Online First: 20160720]\\u003c/li\\u003e\\n\\u003cli\\u003eLarsen DE, Rootman I. Physician role performance and patient satisfaction. \\u003cem\\u003eSoc Sci Med (1967)\\u003c/em\\u003e 1976;10(1):29-32. doi: 10.1016/0037-7856(76)90136-0\\u003c/li\\u003e\\n\\u003cli\\u003eFitzpatrick R. Surveys of patient satisfaction: II--Designing a questionnaire and conducting a survey. \\u003cem\\u003eBmj\\u003c/em\\u003e 1991;302(6785):1129-32. doi: 10.1136/bmj.302.6785.1129\\u003c/li\\u003e\\n\\u003cli\\u003eMendoza Aldana J, Piechulek H, al-Sabir A. Client satisfaction and quality of health care in rural Bangladesh. \\u003cem\\u003eBull World Health Organ\\u003c/em\\u003e 2001;79(6):512-7.\\u003c/li\\u003e\\n\\u003cli\\u003eWu AW, Gifford A, Asch S, et al. Quality-of-Care Indicators for HIV/AIDS. \\u003cem\\u003eDisease Management and Health Outcomes\\u003c/em\\u003e 2000;7(6):315-30. doi: 10.2165/00115677-200007060-00003\\u003c/li\\u003e\\n\\u003cli\\u003eKhan K, Khan S, Qureshi Z, et al. Client Satisfaction towards Quality of Health Services: An Assessment at Primary Healthcare of District Gujranwala. \\u003cem\\u003eInternational Journal of Public Health Science (IJPHS)\\u003c/em\\u003e 2017;6:7-12. doi: 10.11591/.v6i1.6526\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"nature-portfolio\",\"isNatureJournal\":true,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Nature Portfolio\",\"twitterHandle\":\"\",\"acdcEnabled\":false,\"dfaEnabled\":false,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"antiretroviral therapy, conflict, HIV, post-conflict, structural equation modelling, satisfaction\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6325865/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6325865/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eThe HIV care services in Tigray have been severely impacted during and after the infamous Tigray conflict, which took place from November 2020 to November 2022. The present study assessed the perception of care through people living with HIV satisfaction towards pharmaceutical services in the post-conflict Tigray, North Ethiopia.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eA cross-sectional survey using exit interviews was conducted, with data captured via Qualtrics XM Software. The study assessed overall satisfaction using 31 indicators across five latent dimensions: provider communication, commitment and respect, medication use information, solving drug problems, and pharmacy environment. Second-order structural equation modeling quantified how these interrelated factors collectively predict satisfaction. Model robustness was verified through fit indices, ensuring the reliability and validity of the findings.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eThe study reveals low overall satisfaction (57.2%) among people living with HIV, with significant gaps in medication use information (49% satisfied) and solving drug problems (50.2%). Structural equation modeling identifies that improving medication use information has the highest impact on satisfaction (68.2% increase per quality unit, β\\u0026thinsp;=\\u0026thinsp;0.682), followed closely by solving drug-related problems (64.4%, β\\u0026thinsp;=\\u0026thinsp;0.644), provider communication skills (59.4%, β\\u0026thinsp;=\\u0026thinsp;0.594), and commitment \\u0026amp; respect (37.7%, β\\u0026thinsp;=\\u0026thinsp;0.377), all statistically significant (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Facility type significantly influenced satisfaction, with health centers outperforming referral hospitals by 128% (β\\u0026thinsp;=\\u0026thinsp;1.281, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), while primary hospitals showed a drastic 83% decrease in satisfaction (β = -1.789, p\\u0026thinsp;=\\u0026thinsp;0.003). Extending refill intervals beyond 3 months increased satisfaction odds 5.6-fold (β\\u0026thinsp;=\\u0026thinsp;1.732, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Interestingly, non-Mekelle residents reported 79% higher satisfaction than Mekelle residents (β\\u0026thinsp;=\\u0026thinsp;0.794, p\\u0026thinsp;=\\u0026thinsp;0.028). The model explained 84.4% variance, with minimal demographic effects (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05).\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eSatisfaction with pharmaceutical services among people living with HIV in Tigray is significantly lower than the national benchmark of 85%, raising concerns about HIV outcomes. Enhancing drug therapy management and optimizing appointment spacing are essential strategies for improving satisfaction during post-conflict rehabilitation. Targeted interventions should prioritize addressing gaps in the provision of medication use information and drug related problem-solving capabilities, particularly in primary hospitals, where satisfaction levels are critically low.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Perceived quality of pharmaceutical HIV services in a post-conflict setting: structural equation modelling in Tigray, Ethiopia\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-31 11:17:38\",\"doi\":\"10.21203/rs.3.rs-6325865/v1\",\"editorialEvents\":[],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"communications-medicine\",\"isNatureJournal\":true,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"commsmed\",\"sideBox\":\"Learn more about [Communications Medicine](http://www.nature.com/commsmed)\",\"snPcode\":\"43856\",\"submissionUrl\":\"https://mts-commsmed.nature.com/cgi-bin/main.plex\",\"title\":\"Communications Medicine\",\"twitterHandle\":\"@commsmedicine\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Communications Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"a62ee822-acac-4a39-a2ef-1396ef16c54a\",\"owner\":[],\"postedDate\":\"October 31st, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[{\"id\":46651062,\"name\":\"Health sciences/Health care/Health services/Rehabilitation\"},{\"id\":46651063,\"name\":\"Health sciences/Health care/Health policy\"}],\"tags\":[],\"updatedAt\":\"2025-10-31T11:17:38+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-10-31 11:17:38\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6325865\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6325865\",\"identity\":\"rs-6325865\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}