In response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and the potential of spread via air travel, many airlines instituted measures to reduce the risk of in-flight transmission. These include mandatory use of face masks, frequent disinfection of the aircraft, encouraging use of hand sanitizers and pre-flight screening. ¹ Despite the high potential of spread of SARS-CoV-2 prior to boarding and during flight, little data are available to determine the risk of returning travellers introducing infection in the destination country. Qatar is home to Hamad International Airport—one of the busiest global travel hubs in the world. First case of SARS-CoV-2 infection in Qatar was detected on 27 February 2020 in a returning traveller. Soon thereafter, several infection control and public health measures were implemented in Qatar and by Qatar Airways, the national carrier of Qatar. We undertook this study to determine the proportion of travellers infected with SARS-CoV-2 infection from various destinations who arrived between 5 August 2020 and 22 February 2021. During this period, travel to Qatar was restricted to Qatari nationals and long-term residents who were returning home.

This conceptual paper proposes Math-CEFR, a reference framework for describing postsecondary mathematical readiness in the context of remedial and gateway placement. Single-score placement tests frequently misclassify students and offer little guidance for instruction. Drawing on the architecture of the Common European Framework of Reference for Languages (CEFR), Math-CEFR separates global decision bands (M0–M5) from a multi-dimensional diagnostic profile covering procedural fluency, conceptual understanding, structural awareness, and transfer. The framework treats readiness as non-linear and pathway-sensitive, distinguishing two common course pathways in postsecondary mathematics: a quantitative reasoning/statistics route and an algebra-to-calculus route. It is accompanied by illustrative descriptors, task-family specifications, and an alignment and local standard-setting protocol. Because no validity evidence yet exists, transparency and defensibility are design goals whose attainment depends on the validation agenda outlined here—they are not established properties of the framework. Math-CEFR is offered as a structural proposal intended to make placement interpretations more transparent and future empirical evaluation feasible, rather than as a finished testing instrument.

Assessment in higher education rests on an evidentiary claim: a submitted product should support a defensible inference about what a student knows, understands, and can do. Generative AI makes that...