A holistic model for student-centered on and off-campus housing – a cross-country evaluation
Bibliographic record
Abstract
Purpose This paper aims to develop a comparative model to evaluate the facilities and services offered by different types of student housing in the European region and assess their coverage of students’ diverse needs. Design/methodology/approach The research involves a content analysis of 167 student accommodation websites across 13 cities in eight European regions, utilizing indicators derived from Maslow’s hierarchy of needs. Findings The study presents an innovative “wheel model” categorizing student needs in university housing into three dimensions: personal, social and intellectual. It identifies gaps in purpose-built student accommodations’ (PBSAs’) current services and provides a roadmap to enhance and support student living and learning environments. Implementing recommendations for student housing will contribute to achieving the Sustainable Development Goals (SDGs) 3, 4 and 11. Practical implications In the proposed framework, physical space, psychological comfort, social integration, academic engagement and personal growth are significant dimensions that students’ housing must address. Overall, a holistic assessment approach provides stakeholders with a clear understanding of the status of student housing facilities and the improvement in students’ quality of life. Social implications This study underscores how PBSA can serve as catalysts for social integration, inclusion and community building. By addressing students’ personal, social and intellectual needs through a holistic “wheel model”, the research highlights the importance of fostering well-being, a sense of belonging and academic engagement. PBSA, when designed with these dimensions in mind, contributes not only to students’ success but also to more equitable and resilient urban communities, aligning with SDGs 3, 4 and 11. Originality/value This paper introduces a novel “wheel model” that reinterprets Maslow’s hierarchy of needs in terms of interconnected personal, social and intellectual dimensions, providing a non-hierarchical, student-centered framework for evaluating student housing. Based on an extensive cross-country analysis, the model offers a practical and adaptable tool for assessing and improving PBSA quality. It shifts the focus from idealized standards to realistic, evidence-based practices that enhance student well-being, learning and social integration—thereby supporting sustainable development in higher education environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".