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Supplementary Online Materials for “They’re Eating Our Pets!”: When Disgust and Perceived Cruelty Combine to Heighten Prejudice (Accepted for publication in the British Journal of Social Psychology)
No full textMotivation Without Slope: ADHD, Demand Avoidance, and the Moralisation of Misfit
No full textModern work and support systems often assume that effort translates into action in a steady and predictable way through routine, reinforcement, and private self-management, embedding this expectation in institutional infrastructure. For many people with ADHD, this expectation does not hold. Traction can be discontinuous and highly context-dependent, and sustained functioning is often achieved through compensatory pathways such as urgency and acute pressure rather than stable routines. While these strategies can enable short-term performance, they can also be costly when chronic stress becomes the main route to action. When institutions treat their motivational expectations as universal, differences in regulation are frequently moralised as laziness, unreliability, or defiance, and people are praised for crisis performance but blamed for later collapse. Drawing on disability studies, this paper frames these dynamics as institutional misfit rather than individual pathology. It introduces “assumed motivational gradient” as a descriptive metaphor for institutional design bias and outlines implications for disability-informed practice
Examining the effect of Video Interaction Guidance (VIG) on Parental Reflective Functioning amongst high-risk parents in Aotearoa, New Zealand
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Ethics Approval
No full textThis component contains documents that showcase ethics approval from Northern Arizona University's IRB
The use of telemedicine for the provision of contraceptive care to adolescents: scoping review
No full textA scoping review to identify and present available information on the use of telemedicine for the provision of contraceptive care to adolescents, including the modalities of telemedicine used, services provided, and the adolescent population served
Constructing Workforce Identity: A Content Analysis of Generation Z’s Discourse on TikTok and Reddit
No full textThis study investigates how Generation Z perceives and experiences the contemporary labor market, focusing on their early professional identities and values as expressed in digital spaces. Using a mixed-method content analysis of 400 user-generated comments from TikTok and Reddit, the research identifies five recurring typologies that reflect this cohort’s relationship with work: the Transactionalists, who treat employment as a financial exchange with minimal emotional investment; the Apathetics, who express existential fatigue and hopelessness; the Practicals, who prioritize stability and financial security; the Solitaries, who favor autonomy and remote work; and the Equalists, who advocate for equity and workplace reform. These typologies, while analytically distinct, often intersect in practice, illustrating the complexity of generational identity. These typologies are situated within a broader cross-cultural context, aligning with global youth movements such as Tang Ping in China, Sampo in South Korea, Satori in Japan, and Taiwan’s Strawberry Generation, all reflecting a shared disengagement from traditional labor ideals. The study demonstrates the analytical potential of social media as a source of candid, real-time labor narratives, revealing Generation Z’s emphasis on mental well-being, fairness, and value-driven employment. The findings suggest a cohesive generational ethos that prioritizes flexibility, dignity, and ethical alignment in professional life. This has significant implications for employers, HR professionals, and policymakers seeking to align organizational practices with the expectations of this emerging workforce. Social media thus emerges as a vital tool for decoding generational labor narratives.
Jechiu, J., 2025. Constructing Workforce Identity: A Content Analysis of Generation Z’s Discourse on TikTok and Reddit. International Journal of Science and Research Archive, 15, pp.143–157
PREDICTION OF DIABETES USING MACHINE LEARNING
No full textToday, the modern way of life for people leads their eating habits more than ever towards
fast-foods and already-to-use items. Diabetes is one of the most lethal diseases in the World.
It is additionally an inventor of various varieties of disorders for example: coronary failure,
blindness, urinary organ diseases etc. In such cases each individual needs to be diagnosed
with diabetes periodically by different blood tests. Such studies add considerable sums of
expense to a large number of individuals and require facilities and time. It is possible to use
machine learning algorithms as computer aided systems to predict if a person is highly
probable to have diabetes or not, in order to reduce huge number of people who require to
take diagnosis blood tests, to save time and money.This paper focuses on developing
predictable model using various machine learning methods such as Decision Tree, Logistic
Regression, naïve bayes algorithm and Random Forests for predicting incident diabetes using
medical records