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Critical pedagogy has the potential to create transformation experiences within early childhood settings, as educators open up children's worlds to explore fairness, equity, and democratic participation across issues of interest and relevance to young children. Using a critical inquiry framework, informed by the work of Paulo Freire, teachers from an Auckland kindergarten provided examples of what the critical pedagogy elements can look like in practice with young children. These illustrative glimpses demonstrate the workability and impact that critical pedagogy can have in an early childhood education setting.

As the COVID-19 virus has spread worldwide, much attention has been paid to its impact on the health and wellbeing of adults, with less attention to how the virus has impacted on young children. This article draws on documentation and video data from a kindergarten in Aotearoa New Zealand. It discusses the working theories of 4 year-old children whose teachers encouraged them to draw, construct images, explain and tell stories about their experiences, ideas and feelings about the virus. A main argument is that children's working theories about the virus, knowledge of the virus and sense of personal control over keeping themselves safe developed over time. Arts-based and storytelling pedagogy were central in enabling children to communicate with others, to be understood themselves and to extend their own understanding.

This article draws from a 2-year TLRI-funded research project looking at how four early childhood centres constructed positive outcomes for children from migrant families. The research questions focused on how the people, places, and practices in early childhood settings support a sense of bicultural belonging to Aotearoa New Zealand, sustain children's connections to homelands and people, and enable them to contribute important cultural aspects from their home country. This article focuses on one of the research settings that participated in this study, Pakuranga Baptist Kindergarten. Observations and staff reflections revealed the ways teachers used the visual arts, storytelling, and play to value children's and their families' cultural funds of knowledge and foster children's sense of belonging in New Zealand.

The COVID‐19 pandemic revealed the pivotal role of digital learning and online lecture videos, leading a shift toward blended and flipped classrooms in the post‐pandemic era. This shift calls for development and (or) refreshment of novel online educational resources, such as Voice‐Over PowerPoint (VOPPT) presentations, specifically designed for asynchronous or synchronous learning methods. However, resource limitations often impede the timely delivery of high‐quality instructional materials. In this descriptive article, the use of AI‐voice cloning technology to automate the creation of VOPPT presentation has been explored. Descripts' Overdub tool, an AI‐voice cloning program, was trained on the 15‐min voice sample of an anatomy professor, and the synthesized voice was used to narrate presentations on inguinal canal and extrahepatic biliary anatomy. The educational use of this novel approach was evaluated based on the second‐year undergraduate medical students' qualitative feedback. Voice similarity analysis using Resemblyzer, an open‐source Python tool, showed a high similarity score of 0.92 between the cloned and original voices. Despite this, students raised concerns about the robotic voice, quick pace, punctuation problems, pronunciation difficulties, and expressed reservations about AI‐generated lecture narration. While the cloned voice closely matched the original, the AI‐generated narration fell short of capturing the nuanced details needed for an effective anatomy instruction. This uncovers AI's current limitations in the educational contexts but establishes a foundation for future progress in AI‐voice cloning technologies aimed at enhancing online educational resources for anatomy and medical education. [ABSTRACT FROM AUTHOR]

Large Language Models (LLMs) have the potential to improve education by personalizing learning. However, ChatGPT‐generated content has been criticized for sometimes producing false, biased, and/or hallucinatory information. To evaluate AI's ability to return clear and accurate anatomy information, this study generated a custom interactive and intelligent chatbot (Anatbuddy) through an Open AI Application Programming Interface (API) that enables seamless AI‐driven interactions within a secured cloud infrastructure. Anatbuddy was programmed through a Retrieval Augmented Generation (RAG) method to provide context‐aware responses to user queries based on a predetermined knowledge base. To compare their outputs, various queries (i.e., prompts) on thoracic anatomy (n = 18) were fed into Anatbuddy and ChatGPT 3.5. A panel comprising three experienced anatomists evaluated both tools' responses for factual accuracy, relevance, completeness, coherence, and fluency on a 5‐point Likert scale. These ratings were reviewed by a third party blinded to the study, who revised and finalized scores as needed. Anatbuddy's factual accuracy (mean ± SD = 4.78/5.00 ± 0.43; median = 5.00) was rated significantly higher (U = 84, p = 0.01) than ChatGPT's accuracy (4.11 ± 0.83; median = 4.00). No statistically significant differences were detected between the chatbots for the other variables. Given ChatGPT's current content knowledge limitations, we strongly recommend the anatomy profession develop a custom AI chatbot for anatomy education utilizing a carefully curated knowledge base to ensure accuracy. Further research is needed to determine students' acceptance of custom chatbots for anatomy education and their influence on learning experiences and outcomes. [ABSTRACT FROM AUTHOR]

Errorless academic compliance training is a graduated, noncoercive approach to treating oppositional behavior in children. In the present study, three teaching staff in a special education classroom were trained to conduct this intervention with three male students diagnosed with autism spectrum disorders. During baseline, staff delivered a range of academic and other classroom requests and recorded student compliance. A hierarchy of compliance probabilities was then calculated, ranging from Level 1 (requests yielding high levels of compliance) to Level 4 (those typically yielding noncompliance). At treatment initiation, teaching staff delivered high densities of Level 1 requests and provided reinforcement for compliance. Subsequent request levels were faded in over time, at a slow enough rate to ensure continued high compliance. By intervention end, all three students demonstrated substantially improved compliance to classroom requests that had commonly yielded noncompliance before intervention. Covariant improvement in on-task skills was also evident. (Contains 1 table and 3 figures.)

The arteriovenous fistula (AVF) is the optimal form of vascular access for end-stage renal failure patients requiring haemodialysis. However, AVFs can fail to mature into a useable conduit or develop recurrent stenoses that result in high surgical re-intervention rates to maintain patency. Here, we performed a narrative review of literature on methods of AVF failure identification such as clinical prediction models, shear stress metrics characterisation with 0D, 1D and 3D computational models and blood flow sound frequency. Clinical risk factors identified by a clinical scoring system fail to predict ongoing AVF status and patency rate. The pulse wave propagation model, although showed positive prospect to support surgical planning, has limited ability in blood flow volume prediction in the anastomotic region. Shear stress metrics were one of the most common indicators to understand flow behaviours in AVF. While transverse wall shear stress seemed promising in characterising multidirectional flow in an AVF, no definitive correlations between shear stress metrics and disease initialisation have been reported. We conclude that further assessment is required to develop a clinical diagnostic marker for AVF failure prediction as no definitive prediction indicator was proposed even though there is an apparent impact of haemodynamic factors in AVF failure. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) modelling has been a useful non-invasive measurement tool to analyse the effects of blood flow in diseased arteriovenous fistula (AVF). To translate CFD findings for clinical use, it is essential to model the AVF as closely as possible to the physiological conditions. The AVF geometry consists of three boundary nodes, which require appropriate flow conditions prescribed to each of them. To this end, we modelled three AVF geometries, with three different, commonly used boundary conditions: velocity based on patient-specific waveforms, assumed flow split condition for the retrograde flow and assumed flow split condition for the antegrade flow. Three patient-specific geometries were acquired from an in-house ultrasound scanning system. The patient-specific waveforms were also obtained during these scanning sessions for the CFD models. WSS distribution and recirculating flows were observed at different locations for the three patient datasets, with different sets of boundary conditions. These effects were prominent at the anastomosis and dependent on the geometry of the AVF. There were also discrepancies between resistance values estimated from the patient-specific CFD models and the Hagen–Poiseuille model. These findings highlight the importance of modelling patient-specific AVFs with appropriate boundary conditions for better identification of diseased AVF. [ABSTRACT FROM AUTHOR]

The letter to the editor discusses the impact of artificial intelligence (AI) on assessment in medical education. It highlights the challenges of maintaining assessment validity in the context of AI-generated content and emphasizes the need to shift towards assessment for learning and assessment as learning. The authors suggest programmatic assessment models and AI-facilitated peer-to-peer learning as strategies to enhance student engagement and critical thinking. They also stress the importance of faculty familiarizing themselves with AI tools to design assessments effectively in an AI-enhanced environment. [Extracted from the article]

Aim: To investigate whether a digital sleep intervention improves child and care giver sleep and psychosocial outcomes. Methods: A total of 120 families with children aged 2–13 years, reporting moderate to severe child behavioural sleep problems, were recruited from a hospital sleep clinic waitlist or the community. Children from non‐English speaking families, with known intellectual disability (IQ < 70) or severe medical problems excluded. Tailored behavioural sleep strategies were delivered to primary care givers via a smart phone app and complementary website. Eligible families completed a baseline questionnaire and child 'sleep check' then received the digital sleep intervention for 5 weeks, and then completed a post questionnaire. Outcomes: care giver report of child sleep as no/mild versus moderate/severe problem over past month (primary outcome); problem child sleep patterns (Brief Infant Sleep Questionnaire or Child Sleep Habits Questionnaire), child temperament, care giver mental health (Kessler 6), care giver sleep, health service use for their child's sleep and time off work/activities to access services. Results: At follow up, care givers reported fewer moderate/severe child sleep problems (84.6–40.7%), improved problem child sleep patterns, better temperament and improved care giver mental health. Care giver sleep quality and quantity remained unchanged. Health service use (averaged over a 6‐month period pre‐ and post‐intervention) fell from 18.9% pre‐ to 14.1% post‐intervention. Conclusion: A digital sleep intervention appears promising in improving sleep in children with moderate/severe behavioural sleep problems, and care giver mental health. It may be a useful alternative to face‐to‐face management of behavioural sleep problems. [ABSTRACT FROM AUTHOR]