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Saraee F, Babaei Aghbolagh M, Jaafaripooyan E, Sajadi H S. Developing a Model for Determining Postgraduate Student Admission Capacity:
A Case Study of Tehran University of Medical Sciences. Iranian Journal of Medical Education 2026; 26 :15-31
URL: http://ijme.mui.ac.ir/article-1-5884-en.html
URL: http://ijme.mui.ac.ir/article-1-5884-en.html
Knowledge Utilization Research Center, University Research and Development Center, Tehran University of Medical Sciences, Tehran, Iran. , hsajjadi@tums.ac.ir
Abstract: (24 Views)
Introduction: Misalignment between postgraduate student admissions and available educational resources can compromise the quality, efficiency, and equity of training programs. In many settings, including Iran, postgraduate admission capacity at medical universities is often determined without a standardized, transparent, and evidence-based mechanism, which may result in over-enrollment, under-enrollment, and inefficient resource allocation. As demand for postgraduate education continues to grow and the determinants of educational capacity become increasingly complex, there is a critical need for systematic approaches that incorporate multiple criteria and constraints. This study aimed to develop and validate an evidence-informed model for determining postgraduate student admission capacity at Tehran University of Medical Sciences.
Methods: This study was conducted in three sequential phases from 2024 to 2025. In the first phase, criteria and constraints affecting postgraduate admission capacity were identified through a comprehensive review of the literature and grey literature, along with semi-structured interviews with 22 key informants, including leaders, faculty members, and policymakers. Relevant documents in English and Persian were retrieved from four English and two Persian databases, as well as Google Scholar, through March 2024, and screened in two stages: title/abstract review and full-text assessment. Data extracted from eligible sources were analyzed using conventional content analysis. In the second phase, the identified criteria were prioritized and weighted using expert input, and a linear programming model was developed to optimize admission capacity while adhering to predefined constraints. The preliminary model was then evaluated and refined through a Delphi process involving 13 experts to ensure validity, feasibility, and policy relevance. In the third phase, the finalized model was applied in a selected faculty at Tehran University of Medical Sciences. Required data for prioritized criteria were collected from institutional sources, and the model outputs were assessed and validated through expert panel discussion.
Results: In the first phase, 51 criteria and 32 constraints influencing postgraduate admission capacity were identified across domains, including human resources, infrastructure, educational processes, and regulatory requirements. In the second phase, 20 key criteria were prioritized and incorporated into a linear programming model designed to maximize admission capacity while maintaining educational quality standards. These criteria included annual intake limits for master’s and doctoral students; classroom and laboratory space; per capita standards; educational support and welfare facilities; faculty composition by academic rank; admission capacity per faculty member; availability of training environments; and departmental ranking based on national scient metric indicators. The Delphi process confirmed the relevance, clarity, and applicability of the proposed model. In the third phase, applying the model to faculty-level data yielded estimated admission capacities of 30 master’s students and 15 doctoral students across 10 academic departments. Experts considered these outputs realistic and aligned with institutional resources and priorities.
Conclusion: The proposed model provides a structured, evidence-informed framework for determining postgraduate admission capacity, accounting for educational quality and resource availability. By integrating quantitative optimization techniques with expert-informed criteria, the model offers a scalable alternative to ad hoc decision-making. Its wider adoption could support more rational, equitable, and accountable policymaking and improve alignment between educational capacity and system needs in medical universities.
Methods: This study was conducted in three sequential phases from 2024 to 2025. In the first phase, criteria and constraints affecting postgraduate admission capacity were identified through a comprehensive review of the literature and grey literature, along with semi-structured interviews with 22 key informants, including leaders, faculty members, and policymakers. Relevant documents in English and Persian were retrieved from four English and two Persian databases, as well as Google Scholar, through March 2024, and screened in two stages: title/abstract review and full-text assessment. Data extracted from eligible sources were analyzed using conventional content analysis. In the second phase, the identified criteria were prioritized and weighted using expert input, and a linear programming model was developed to optimize admission capacity while adhering to predefined constraints. The preliminary model was then evaluated and refined through a Delphi process involving 13 experts to ensure validity, feasibility, and policy relevance. In the third phase, the finalized model was applied in a selected faculty at Tehran University of Medical Sciences. Required data for prioritized criteria were collected from institutional sources, and the model outputs were assessed and validated through expert panel discussion.
Results: In the first phase, 51 criteria and 32 constraints influencing postgraduate admission capacity were identified across domains, including human resources, infrastructure, educational processes, and regulatory requirements. In the second phase, 20 key criteria were prioritized and incorporated into a linear programming model designed to maximize admission capacity while maintaining educational quality standards. These criteria included annual intake limits for master’s and doctoral students; classroom and laboratory space; per capita standards; educational support and welfare facilities; faculty composition by academic rank; admission capacity per faculty member; availability of training environments; and departmental ranking based on national scient metric indicators. The Delphi process confirmed the relevance, clarity, and applicability of the proposed model. In the third phase, applying the model to faculty-level data yielded estimated admission capacities of 30 master’s students and 15 doctoral students across 10 academic departments. Experts considered these outputs realistic and aligned with institutional resources and priorities.
Conclusion: The proposed model provides a structured, evidence-informed framework for determining postgraduate admission capacity, accounting for educational quality and resource availability. By integrating quantitative optimization techniques with expert-informed criteria, the model offers a scalable alternative to ad hoc decision-making. Its wider adoption could support more rational, equitable, and accountable policymaking and improve alignment between educational capacity and system needs in medical universities.
Type of Study: Original research article |
Subject:
Educational Management
Received: 2025/08/11 | Accepted: 2026/02/9 | Published: 2026/04/30 | ePublished: 2026/04/30
Received: 2025/08/11 | Accepted: 2026/02/9 | Published: 2026/04/30 | ePublished: 2026/04/30
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