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To meet the needs of under-resourced, rural schools where teacher attrition is high, this case study examined how a school–university partnership strengthened teacher and leader abilities to support deeper learning for students.

This research focused on a 17-year collaborative partnership between one rural school district and a university research and outreach organization to develop deeper learning experiences for students through shared and supportive leadership and learning of teachers and leaders. The researchers utilized documents, field notes and interviews with administrators to validate the data.

The study’s findings suggest that participation in authentic, researched-based professional development through the partnership improved the skills of leaders and teachers to support deeper learning for students. This partnership heightened teacher and leader capacity to promote and support continued change and sustainability.

This case study explored how one university center collaboratively engaged with a district by sharing research and strategies to support the development of leaders and teachers to create deeper learning for students. Through these experiences, the district evolved its deeper learning system and improved its organizational effectiveness, leadership development and learning for all.

Being an efficient mechanism for the value of money, public–private partnership (PPP) is one of the most prominent approaches for infrastructure construction. Hence, many controversies about the performance effectiveness of these delivery systems have been debated. This research aims to develop a novel performance management perspective by revealing the causal effect of key performance indicators (KPIs) on PPP infrastructures.

The literature review was used in this study to extract the PPPs KPIs. Experts’ judgment and interviews, as well as questionnaires, were designed to obtain data. Copula Bayesian network (CBN) has been selected to achieve the research purpose. CBN is one of the most potent tools in statistics for analyzing the causal relationship of different elements and considering their quantitive impact on each other. By utilizing this technique and using Python as one of the best programming languages, this research used machine learning methods, SHAP and XGBoost, to optimize the network.

The sensitivity analysis of the KPIs verified the causation importance in PPPs performance management. This study determined the causal structure of KPIs in PPP projects, assessed each indicator’s priority to performance, and found 7 of them as a critical cluster to optimize the network. These KPIs include innovation for financing, feasibility study, macro-environment impact, appropriate financing option, risk identification, allocation, sharing, and transfer, finance infrastructure, and compliance with the legal and regulatory framework.

Identifying the most scenic indicators helps the private sector to allocate the limited resources more rationally and concentrate on the most influential parts of the project. It also provides the KPIs’ critical cluster that should be controlled and monitored closely by PPP project managers. Additionally, the public sector can evaluate the performance of the private sector more accurately. Finally, this research provides a comprehensive causal insight into the PPPs’ performance management that can be used to develop management systems in future research.

For the first time, this research proposes a model to determine the causal structure of KPIs in PPPs and indicate the importance of this insight. The developed innovative model identifies the KPIs’ behavior and takes a non-linear approach based on CBN and machine learning methods while providing valuable information for construction and performance managers to allocate resources more efficiently.

Demonstrates the application of spreadsheets in simulating queuing systems with arrivals from a finite population. The problem is referred to as the machine repair problem where the members of the queue are machines that are breaking down and the servers are the technicians repairing the broken machines. The total number of machines are finite and pre‐specified. The technique for the development of the simulation is illustrated with six machines. Describes the approach for developing a generalized simulation model with any number of machines.

Studies show that economic sanctions have had major negative impacts on health systems during the past years. The aim of this study is to identify the impacts of US sanctions on the performance of public hospitals in Iran.

A qualitative research study was conducted between October 2019 and September 2020 in Kermanshah Province, Iran. Semi-structured, face-to-face interviews, lasting between 25 and 90 min, were carried out with 20 participants in seven public hospitals affiliated to the authors’ institution in Kermanshah Province. Inductive thematic analysis was used to identify themes in the data.

Five main themes emerged from the analysis: resource management challenges; financial restrictions; interruptions in planning; reductions in the quality of service delivery; and changes in organizational relationships.

The results of the present study demonstrate that US economic sanctions have considerably reduced access to necessary medical equipment and medicines for public hospitals in Iran. Policymakers should monitor the distribution of equipment and pharmaceutical products within public hospitals in Iran and take actions to ameliorate shortages during times of economic sanctions.

Appropriate access to formally-trained health workers for people living in rural and underdeveloped areas is a continuing challenge worldwide. The purpose of this paper is to investigate the willingness of formally-trained health workers to practice in underdeveloped areas and its main determinants among medical students in the western provinces of Iran.

A total of 753 medical students from four provinces in western Iran (Kermanshah, Ilam, Lorestan and Kurdistan) were surveyed cross-sectionally in 2017. A self-administrated questionnaire was used to collect data on sociodemographic characteristics, willingness to practice in underdeveloped areas, intrinsic (e.g. desire to help others and self-interest in medicine) and extrinsic (e.g. the high income of physicians and social prestige) motivations of the study population. Multivariable logistic regression was used to identify the main determinants of willingness to practice in underdeveloped areas among medical students after their graduation.

The results indicated that 58.3 percent of students were willing to practice in underdeveloped areas. While 59 percent of the study population had a strong extrinsic motivation to study medicine, the remaining 41 percent of the study population had a strong intrinsic motivation to study medicine. The logistic regression results indicated that low parental professional and educational status, an experience of living in rural areas and having strong intrinsic motivation were associated with greater willingness to practice in underdeveloped areas.

This is the first study to investigate the willingness to practice in underdeveloped areas and its main determinants among medical students in the west of Iran.

The purpose of this paper (systematic review and meta-analysis) is to synthesize and analyze studies that assessed Iranian hospital efficiency.

A systematic literature search was conducted using both international (the Institute for Scientific Information, Scopus and PubMed) and Iranian scientific (Magiran, IranMedex and Scientific Information Database) databases. The review included original studies that used the Pabon Lasso Model to examine Iranian hospital performance, published in Persian or English. A self-administered checklist was used to collect data. In total, 12 questions were used for quality assessment.

In total, 34 studies met our inclusion criteria. The fixed-effects meta-analysis indicated that 19.2 percent (95% confidence interval (CI): 15.6–23.2 percent) of hospitals were in Zone 1 (poor performance: low bed turnover rate (BTR) and bed occupancy rate (BOR) and high average hospital stay (ALoS)), 23.7 percent (95% CI: 20.1–27.8 percent) were in Zone 2, 31.7 percent (95% CI: 27.7–36 percent) in Zone 3 (good performance: high BTR and BOR and low ALoS) and 25.4 percent (95% CI: 21.7–29.5 percent) in Zone 4.

Results help Iranian health policymakers to understand hospital performance, which, in turn, may lead to promoting greater awareness and policy attention to improve Iranian hospital efficiency.

This study indicated that most Iranian hospitals had sub-optimal performance. Further studies are required to understand factors that explain the country’s hospital inefficiency.

Poor health-related quality of life (HRQoL) is one of the important issues in the health sector. The purpose of this paper is to investigate the prevalence and socio-economic inequality in poor HRQoL in Tehran city, Iran.

In total, 562 adults were included in this cross-sectional study. The cluster sampling method was used for data collection from May to June, 2016 in Tehran city, Iran. Data on HRQoL, using EuroQol 5-dimensions questionnaire, and data on socio-economic and demographic variables were gathered. Convenience regression method was performed to measure the concentration index (CI). Decomposition analysis was performed to determine the contribution of variables on socio-economic inequality in poor HRQoL. All analyses were performed by Stata v.14.

The prevalence of poor HRQoL was 28.3 percent. The value of CI for “poor HRQoL” was −0.299 (95% confidence interval: −0.402 to −0.195). Socio-economic status (SES) was the largest contributor to socio-economic inequality in poor HRQoL (69.44 percent of inequality was explained by SES). Age, obesity and race had a positive contribution to socio-economic inequality in poor HRQoL among the participants. Nonetheless, sex and smoking intensity had a negative contribution to inequality in poor HRQoL.

There is little evidence about the prevalence of poor HRQoL in insured people. This study provided new evidence in this area through the investigation of socio-economic inequality in poor HRQoL and its determinants among people with health insurance in Iran using decomposition analysis.