Background. Access to primary healthcare remains a public health concern for international students in Quebec. Despite their legal status and contributions to the academic system, these students face multiple barriers that hinder their use of health services. Objective. This study aims to document the main barriers to healthcare access and identify opportunities for improving institutional and community-based practices. Methods. A mixed-methods approach was used, combining a literature review, an online survey of 85 international students, and thematic analysis of open-ended responses. The analysis was guided by Levesque et al.’s (2013) conceptual framework on access to healthcare. Results. Nearly 67% of participants reported difficulties accessing care, mainly related to service costs, inadequate private insurance, administrative complexity, and lack of information. Telehealth acceptability was high (74%), and over 80% of respondents reported experiencing stress related to accessing healthcare. Conclusion. The findings highlight the urgent need to adapt health services and welcoming policies to the specific needs of international students in order to promote health equity in the Quebec academic and migratory context.

We often witness a process of « anchoring » teaching objects in students’ memories, where risk-free learning is favored over action, where contemplation is advocated over action, and where students are made to feel alienated from the master as the holder of knowledge, without any concern for creating the need for intellectual nourishment and a thirst for learning. The aim of this article is to show the value of cultivating astonishment as a risk for language learners, in order to give rise to a sense of responsibility and subjectivity associated with risk-taking as a source of progress and innovation, rather than one of alienation, which would prevent the movement of being and thinking.

This article examines the epistemological articulations between geography and sustainable development (SD) in the context of the Sustainable Development Goals (SDGs) adopted by the United Nations in 2015. As a global normative framework for a prosperous yet sustainable world, the SDGs, when confronted with the challenge of their territorialization, revive the idea of a «Geography of Sustainable Development» (GSD), a notion debated since the 2000s but which seems to have remained stagnant despite intersections around the human–nature relationship, the question of scale, and systemic approaches. Why this reluctance to affirm a GSD? Does the territorialization of the SDGs provide a new analytical lens for geography? What contributions can geographers make to the implementation of the SDGs? Rather than claiming the existence of a GSD, our aim is to highlight the relevance of geography in scaling the 2030 Agenda. This work draws on a critical review of the literature, an analysis of global and national reports, as well as data from a doctoral thesis and interviews conducted in 2024 and 2025 within the framework of a postdoctoral fellowship supported by the Global Development Network (GDN). The findings highlight the enduring presence of SD in geography, the significant role of geographical objects in the territorial anchoring of the SDGs, and territorialization as an approach that gives meaning to a GSD.

Explainable Artificial Intelligence (XAI) is essential for deploying complex Computer Vision (CV) models in areas such as medical diagnosis, where transparency and accountability are required. This paper explores a hybrid interpretability framework that balances faithfulness, how well the explanation matches the model’s decision, and computational efficiency. We assess three main types of XAI: attribution-based (Grad-CAM), perturbation-based (RISE), and transformer-based attention methods. Studies show that perturbation-based methods such as RISE achieve the highest fidelity (Insertion AUC 0.727, Pointing Game Accuracy 91.9%), but they are too slow for real-time clinical use (0.05 FPS). Transformer-based XAI methods, by contrast, align more closely with expert annotations in medical tasks (IoU 0.099) and operate at a moderate speed (25.0 FPS). We suggest combining the localisation accuracy of attention-based models with the efficiency needed in clinical settings to create high-quality, useful saliency maps for medical diagnosis.