The University of Cumbria is a public university in Cumbria, with its headquarters in Carlisle and other major campuses in Lancaster, Ambleside, and London. It has roots extending back to the Society for the Encouragement of Fine Arts, established in 1822, and the teacher training college established by Charlotte Mason in the 1890s. It opened its doors in 2007 as a university.
Social prescribing is an increasingly well-known international model of healthcare, yet there is little agreement about its role and efficacy. This paper comprises a secondary analysis of findings from service users in a small-scale (n.8) qualitative study in the English Midlands using the approach of candidacy, a comprehensive framework with the potential for achieving a deeper understanding of social prescribing. Participants were accessed through local social prescribing link workers, who were located within general practitioner services. A candidacy approach recognises that the ways in which individuals perceive their health needs are key to understanding how or why they assert a claim to be a candidate for services. The aim of the paper is to add to the voices of lived experience within social prescribing and to explore whether the application of a candidacy framework brings about new insights. The original study on which this paper is based employed a mixed-methods approach in one clinical commissioning group area with a significant rural population. The secondary analysis brought forth three new themes not originally illuminated-accessibility of the service (permeability), financial constraints (appearances) and relationships with social prescribing link workers (adjudications).Recommendations are that communities, and especially rural ones, need to be made more aware of their local social prescribing offers, that social prescribing staff take fuller account of individuals' contexts and that healthcare staff need to be clear about how services are explained. Further larger-scale research is needed to demonstrate the ways in which candidacy theory can remain relevant within increasingly digitalised and complex health services where the opportunities for face-to-face negotiation are limited.
This commentary investigates how generative AI tools such as DALL-E can create imagery which (re)produce racist, gendered and classist representations of peoples. Drawing on prompts entered across three time periods into DALL-E, I employ algorithmic coloniality as a conceptual framework, together with critical visual analysis, critical race semiotics and intersectionality to examine the images created. This analysis shows that, despite advances in photorealism, DALL-E persistently reproduces the same racialised and gendered tropes in its depictions of Black American women. I argue that these images are not merely aesthetic by-products, but socio-technical artefacts shaped by historically racist training data. Moreover, I suggest that enhanced photorealism may amplify, rather than mitigate, such stereotypes. Building on these findings, I argue that geography educators need to cultivate forms of critical AI literacy that extend beyond refining prompts to interrogate the algorithmic coloniality embedded within these systems. I conclude by proposing practical and collective strategies to support geography educators engaging with these tools.
For much of the sporting past, animal-human relationships have been central to many of England's more popular sports, often encouraging sociability, gambling, and commercialisation. Yet historians of sport are yet to fully initiate studies of animal-human inter-relationships. Cockfighting provides just one example. It was a leading sport in England from the seventeenth to the early nineteenth century. For its more professionalised and commercialized cock-match mains, expert 'cock-feeders' were employed to train, feed and prepare gamecocks for combat. Most were publicly available, hiring out their specialized knowledge, skills and expertise. The social origins, careers, and the skills and expertise of the profession, the complexities of the work done by feeders with their charges, and how it changed over time, in terms of exercise and sparring regimens, the length of training, the nature of cock diets, and weight reduction techniques, serves as a strong reminder that cock-feeders and the gamecocks both had agency, and that relationships between gamecocks and humans were two-way affairs, as the birds responded to their feeders and the feeders to their charges.
This study presents a systematic review of 107 peer-reviewed articles on succession planning in African family businesses, offering a conceptual reframing of succession as an institutionally embedded process rather than a discrete managerial task. Moving beyond proceduralist and Eurocentric paradigms, the review integrates institutional theory, socioemotional wealth, and dynamic capabilities to interrogate how cultural norms, economic constraints, and social expectations interact to shape succession outcomes. Findings reveal a dominance of informal, kinship-based succession practices that, while culturally coherent, often compromise gender inclusion, strategic renewal, and organizational resilience. This study introduces a Context-Mechanism-Outcome (CMO) framework that synthesizes how succession success is mediated by the alignment between contextual forces and formal/informal planning mechanisms. Underrepresented subregions (e.g., Francophone and matrilineal societies) and overlooked themes (e.g., digital succession, gendered agency, and advisory ecosystems) are identified as critical frontiers for future research. The review concludes by proposing a theoretically generative agenda built on five propositions that reconceptualize succession planning through the lenses of institutional hybridity, temporal processuality, and intersectional legitimacy. This work provides a foundational synthesis for scholars and a diagnostic roadmap for practitioners seeking to structure inclusive, culturally attuned, and future-ready succession strategies in Africa.
Background Incorrect artificial intelligence (AI) suggestions can lead to automation bias; however, their impact on medical image interpretation is underresearched. Purpose To assess how incorrect AI suggestions influence the diagnostic accuracy, read times, and visual search behavior of readers interpreting screening mammograms. Materials and Methods In this retrospective multireader paired study conducted between September 2024 and February 2025, 10 National Health Service Breast Screening Programme mammography readers evaluated a test set of two-view mammography screening examinations. The test set included true-positive (TP), false-negative (FN), false-positive (FP), and true-negative (TN) AI suggestions, verified by 3 years of follow-up or histopathologic analysis. In round 1, readers interpreted cases without AI. In round 2, conducted 6 weeks later, a commercially available AI tool was used as decision support, displaying prompts with a region score of 10 or higher (scale, 0-100). Eye-tracking cameras recorded readers' fixations-maintained gaze-over specific image areas. Wilcoxon signed rank tests were used for paired comparisons between rounds, and Kruskal-Wallis tests compared cases with different AI outcomes. Results The test set (n = 60) included cases with 26 TP, 14 FN, 14 FP, and six TN AI suggestions. Median reader sensitivity was lower for cases with FN AI suggestions when reading cases with AI (39%) compared with unassisted reading (71%; P = .002). Reader specificity was higher for cases with FP AI suggestions (39% vs 21%; P = .004). A greater number of visible (TP and FP) AI prompts led to longer median read times, from 25 seconds (zero prompts) to 34 seconds (four or more prompts) (P = .001). Readers fixated less when reviewing cancer cases that AI failed to detect (FN suggestions) compared with unassisted reading (0.44 vs 0.47 fixations per second; P = .03). Shorter fixation durations were observed when readers interpreted cases with FP AI suggestions compared with unassisted reading (0.54 vs 0.56 second; P = .001). Conclusion Incorrect AI suggestions influenced both reader accuracy and visual search behaviors during mammography interpretation. The greatest negative impact was observed with FN AI suggestions; therefore, AI thresholds should be calibrated accordingly. © RSNA, 2026 Supplemental material is available for this article. See also the editorial by Clauser in this issue. See also the editorial by Abbasi and Giess in this issue.
