Scarborough Health Network (SHN) is a hospital network in Scarborough, Toronto, Ontario, Canada. It operates the Scarborough General, Centenary, and Birchmount hospitals. The three are major community health hospitals with teaching affiliations to the University of Toronto Faculty of Medicine.
Two-sided platforms, such as labor marketplaces for hiring freelancers, typically generate revenue by matching prospective buyers and sellers and extracting commissions from completed transactions. Disintermediation, where sellers transact off-platform with buyers to bypass commission fees, can undermine the viability of these marketplaces. Although circumventing the platform allows sellers to avoid commission fees, it also leaves them fully exposed to risky buyers (given the absence of the platform's payment protections) and incurs switching costs (given the absence of the platform's transaction infrastructure). In this paper, we consider interventions for addressing disintermediation, focusing on the pricing and informational levers available to the platform, where the latter refers to the accuracy of the signal sellers receive about buyers' riskiness. First, whereas intuition suggests platforms should counter disintermediation by lowering commission rates, in a high-information environment, a platform may be better off raising them. Further, when information quality is high, an increase in sellers' switching costs may hurt platform revenue. Finally, a platform may strictly benefit from sellers receiving a partially informative buyer signal (i.e., not perfectly revealing nor concealing a buyer's riskiness), particularly when switching costs are low. As extensions, we examine the efficacy of two interventions: implementing platform access fees to capture revenue upfront and banning sellers caught disintermediating. Overall, our results shed light on how disintermediation disrupts platform operations and offer prescriptions for platforms seeking to counteract it.
The rise of generative AI (GenAI) in higher education has prompted urgent debates surrounding academic integrity and ethical use. This study examines cross-cultural differences in student perceptions of GenAI use, comparing responses from students at Canadian and South Korean universities. Using a scenario-based survey administered in Fall 2024, we analyzed how students judged the ethicality and rule compliance of AI-assisted coding practices. Results reveal that Canadian students were consistently more likely to perceive the use of GenAI as both unethical and against institutional policies compared to Korean students, despite functionally identical institutional policies. Statistical analysis, including Mann-Whitney U tests and correlation coefficients, demonstrated significant differences across nearly all scenarios. Analysis of the factors used in generating scenarios indicated that the amount of AI-generated code incorporated into assignments most strongly influenced ethical judgments. Findings were interpreted through Hofstede’s cultural dimensions framework, suggesting that cultural factors such as power distance, individualism, and uncertainty avoidance significantly shape students’ ethical reasoning regarding GenAI. Our results contribute to the growing body of evidence emphasizing that equitable AI integration in education must be culturally responsive, taking into account diverse conceptions of academic integrity. We advocate for the development of nuanced AI-use guidelines that are sensitive to local cultural contexts while upholding fundamental principles of academic honesty. This study highlights the need for ongoing cross-cultural research to inform ethical AI policies and support responsible GenAI use in global higher education settings.
INTRODUCTION: Dysphagia frequently occurs in movement disorders, leading to malnutrition and aspiration. Percutaneous endoscopic gastrostomy (PEG) provides nutrition directly into the stomach, bypassing the dysfunctional swallow. However, PEG insertion is a complex decision, both clinically and ethically. Although PEG outcomes are reported in other neurological disorders, there is limited research in atypical parkinsonian syndromes such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). Insertion rates remain variable, reflecting a paucity of research and lack of consistent guidelines. Basic mortality and morbidity data would help inform practice. To our knowledge, this is the first international study of PEG insertion and its impact on survival and aspiration pneumonia in atypical parkinsonian syndromes. METHOD: This was an international retrospective study of 72 patients with MSA, PSP or CBD. Survival was recorded from reported onset of dysphagia to death. Secondary outcomes included hospital admission rate for aspiration pneumonia. RESULTS: Median survival was 17.4 months (95% confidence interval [CI] 14.0-24.9) in non-PEG patients versus 48.8 months (95% CI 44.8 to not reached) in PEG patients, hazard ratio (HR) 0.38 (95% CI 0.18-0.81; p=0.013). PEG was not associated with reduced risk of aspiration pneumonia; 0.76 versus 0.68 admissions per patient-year, incidence rate ratio (IRR) 1.41 (95% CI 0.74-2.68; p=0.297). CONCLUSION: PEG insertion may improve survival in atypical parkinsonian syndromes, though we found no evidence of reduced aspiration risk. Given the rarity of these conditions, international registries may help to determine the safety and efficacy of PEG use.
Accurate forest carbon (C) estimation is critical for understanding the role forests play in the global C cycle. Forest C estimation relies on wood carbon fractions (CF) – the proportion of dry wood that is comprised of elemental carbon – in order to convert estimates of tree biomass into C stock estimates, which are then upscaled to estimate forest C stocks at larger spatial scales. Generic wood CFs are often used in C estimation frameworks, despite evidence suggesting this trait varies widely across species, and that this variability influences our understanding of C stocks in trees and forests. Here, we couple data from over 39 000 trees in a 13.5 ha forest dynamics plot in central Ontario, Canada, with open-access wood CF databases, to quantify how wood CFs influence C stock estimates from the individual tree through to 400 m2 and 1 ha forest ecosystem scales. In comparison to generalized wood CF assumptions (e.g., assuming a 50 % CF or using wood CFs from the Intergovernmental Panel on Climate Change), species-specific wood CFs significantly influence C estimates at multiple scales. In comparison to species-specific wood CF data, tree-level estimates derived from other wood CF assumptions were biased by 0.8–3.9 kg C per tree on average, with differences ranging up to >500 kg C in large trees. While relatively small, these tree-level differences compound at larger spatial scales, with C stocks estimated using generalized wood CFs differing by 1.3–3.2 Mg C ha−1 on average vs. those generated using species-specific wood CFs. These forest-scale discrepancies in C estimates increase in forest stands with high amounts of aboveground biomass in large trees and greater proportions of conifers, in some instances exceeding 23.5 Mg C ha−1 in especially biomass-dense conifer-dominated forest stands. When extrapolated to the temperate forest biome, our results indicate that a 50 % wood CF assumption – historically and presently one of the most common methodological assumptions in forest C research – overestimates global C stocks by 2.2–2.5 Pg C. Our study is among the first to examine how wood CF assumptions influence tree- and forest-scale C estimation. We specifically demonstrate that species-specific wood CF data – especially for species that comprise the largest trees – are critical to ensuring accurate C stock estimates derived from forest and tree inventory data.
