Care Purchasing & Brokerage

Assignment Support Day material

Parental insights on the effects of Social Media Exposure and Surveillance Awareness on the Mental Health of Youth in India

The increasing prevalence of social media in India has reshaped adolescents’ digital behaviors, offering both opportunities for growth and significant risks to mental health. This dissertation explores Indian parents’ perspectives about the psychological and behavioral impacts of social media exposure on their children, with a particular focus on surveillance awareness and privacy concerns. Through a qualitative, interpretive approach, this study draws insights from semi-structured interviews with six Indian parents, supported by secondary data from relevant academic studies and reports. The findings reveal that platforms like YouTube, Instagram, and Snapchat dominate adolescents’ online interactions, fulfilling educational, social, and entertainment needs. However, social media exposure also contributes to emotional distress, dependency behaviors, and privacy-related challenges. Parents expressed heightened concerns about their children's addiction to social media and its influence on mood swings, anxiety, and social withdrawal. Limited awareness of data tracking and algorithmic surveillance further hindered their ability to mitigate risks, with many relying on restrictive mediation strategies such as screen-time limits, which often strained parent-child relationships. This study underscores the critical need for culturally informed interventions to empower parents in navigating the digital landscape. Recommendations include improving parental digital literacy, integrating privacy education and online ethics into school curricula, and enforcing stricter algorithmic transparency to safeguard adolescents’ well-being. By addressing these challenges, families can foster healthier digital engagement and promote resilience in an increasingly connected world

Neural correlates of lexical, sublexical and motor processes in word handwriting

Writing recruits a vast neural network underpinning both linguistic and motor processes. Previous studies have tried to identify which brain areas underpin both the linguistic and motor aspects of writing. However, little is known about the neural substrate of the lexical and sublexical “routes” for spelling. In this fMRI study, participants (n = 25) copied or saw/read symbols or words. Words varied in lexical frequency and phonology-to-orthography (P-O) consistency. Anterior parts of the inferior frontal gyrus were selectively recruited when copying P-O inconsistent words, while the right Heschl's gyrus was recruited only when copying consistent words. Non-specific motor and linguistic areas were also identified. Our results contribute to our knowledge of the neural substrate of the lexical and sublexical spelling routes and suggest that different brain areas might be involved in the lexical processing of input (reading) and output (writing) orthography

Social learning under ambiguity : an experimental study

Many behaviours spread through contact with others. The extent to which people adopt observed behaviour can critically affect whether policymakers are successful when introducing new initiatives. In many situations, people can either make decisions based on their own intuitive signals or follow a social signal. Depending on the quality of the signals, one might be more informative than the other. This study aims to better understand how people use social information to learn in ambiguous situations, when both the private and the social signal are not perfectly informative. We conduct an experimental study that observes whether people are prone to imitate others in risky and ambiguous environments. We find that individuals do learn from social information and that this learning is robust and not significantly affected by ambiguity

A cross-sectional study comparing passive and eccentric modes of an isokinetic dynamometer to assess eccentric torque in trained athletes : methodological considerations

Some technical limitations to using the eccentric mode to measure peak eccentric strength of the hamstrings (PTHecc) were raised. PTHecc also has limited validity to predict performance or injury risk factor. Therefore, our aim was to compare PTHecc and other isokinetic variables tested in the eccentric and passive modes. Twenty male hockey players (20.2 ± 1.1 years; 179.7 ± 6.9 cm; 73.4 ± 7.1 kg and 12.2 ± 3.4% of body fat) performed maximal eccentric contractions of the hamstrings at 60°·s−1 (three repetitions) and 180°·s−1 (five repetitions) on both legs and using the eccentric mode and the passive mode (automatic movement of the lever arm) of the Biodex System 4 isokinetic dynamometer. The following variables were measured: PTHecc, the angle of peak torque (APT,°), angle-specific Hecc torque at intervals of 10° and the rate of torque development (RTD) in the first 50 ms and the first 100 ms. The main results showed that compared to the eccentric mode, the passive mode led to a significantly greater PTHecc in the non-dominant leg only and significantly smaller APT, RTD and angle-specific Hecc at angles close to knee flexion. In contrast, significantly greater angle-specific Hecc was observed in the passive mode at angles close to extension (10°–40°). This suggests that, although eccentric or concentric modes can be used to compare isokinetic data to existing literature, it is preferable to use the passive mode to assess peak torque or torque close to knee extension. The eccentric mode might be better to assess variables at the start of movement such as RTD

The interplay between carbon storage, productivity, and native tree density of agroforestry systems

Agroforestry has been widely suggested as a tool for storing carbon while also providing other ecosystem services like food and income production. A greater understanding of how carbon storage in agroforestry systems varies, and particularly how it is intertwined with the productivity of these systems, could enable farmers and policymakers to make changes that simultaneously increase carbon storage and alleviate poverty. In this study, we used allometric equations to evaluate the carbon storage in the biomass of two complex agroforestry systems in Bali, Indonesia—rustic where a native tree canopy is still present, and polyculture where all native trees have been removed, and the canopy consists only of cropping trees. We then compared these figures to that of a nearby primary forest and linked carbon storage to productivity for both agroforestry systems. We found that the primary forest (277.96 ± 149.05 Mg C ha−1) stored significantly more carbon than either the rustic (144.72 ± 188.14 Mg C ha−1) or polyculture (105.12 ± 48.65 Mg C ha−1) agroforestry systems, which were not significantly different from each other. We found productivity and carbon storage to be significantly positively correlated with each other within the polyculture system but not within the rustic system. We also found that for the rustic system, an increase in the density of native trees is accompanied by an increase in carbon storage, but no significant change in productivity. Consequently, we conclude that within the rustic system, carbon storage can be increased or maintained at a high value by the preservation and encouragement of large native trees, and that this need not necessarily result in a decrease in productivity

Annual review article: Rethinking heteronormativity in entrepreneurship studies research

This review article critiques how entrepreneurship scholars have engaged with heteronormativity. Research on entrepreneurship and heteronormativity is emergent and largely confined to the literature on lesbian, gay, bisexual, trans and queer (LGBTQ) entrepreneurs. Despite generating valuable insights into heteronormativity’s impact on LGBTQ entrepreneurs, there is unevenness in how it is conceptualised in terms of definition and deployment. Elaborated in a set of critical observations, this article argues that the choices entrepreneurship scholars make about how heteronormativity is (not) defined and utilised are clearly consequential for any analysis of its dynamics in and outside the entrepreneurship domain. To progress debate and research, this article conceptualises heteronormativity as analytical category that is steeped in queer and feminist theory. This review calls for clearer and deeper conceptual engagements with heteronormativity and a more encompassing approach to the study of heteronormativity that also focuses on its relationship with heterosexuality and heterosexual entrepreneurs

Entrepreneurial finance and the survival of equity-funded firms in crisis periods : the case of COVID-19

This study investigates the resilience of 13,786 UK entrepreneurial firms that received equity financing before COVID-19, with 653 becoming insolvent and 6254 securing guaranteed loans during the pandemic. Utilising the resource-based view (RBV) and signalling theories, we hypothesise that equity-backed firms have sufficient resources to withstand crises, varying by investor type and involvement. We compare the bankruptcy risk of these firms during COVID-19 to the pre-COVID period, considering investor type, deal history and financial and non-financial factors. Results show similar insolvency rates during COVID-19 compared to pre-COVID, but firms backed by active investors are less likely to become insolvent during crises. We examine the characteristics of loan recipients, financing combinations and insolvency risk, finding that companies using COVID loans were generally more prone to insolvency, except those backed by active investor types. Our findings offer insights into the role of equity financing across various investor types in venture survival during crises, with policy implications

CreINNS : credal-set interval neural networks for uncertainty estimation in classification tasks

Effective uncertainty estimation is becoming increasingly attractive for enhancing the reliability of neural networks. This work presents a novel approach, termed Credal-Set Interval Neural Networks (CreINNs), for classification. CreINNs retain the fundamental structure of traditional Interval Neural Networks, capturing weight uncertainty through deterministic intervals. CreINNs are designed to predict an upper and a lower probability bound for each class, rather than a single probability value. The probability intervals can define a credal set, facilitating estimating different types of uncertainties associated with predictions. Experiments on standard multiclass and binary classification tasks demonstrate that the proposed CreINNs can achieve superior or comparable quality of uncertainty estimation compared to variational Bayesian Neural Networks (BNNs) and Deep Ensembles. Furthermore, CreINNs significantly reduce the computational complexity of variational BNNs during inference. Moreover, the effective uncertainty quantification of CreINNs is also verified when the input data are intervals