Conectar
To evaluate how the codesigned training programme, ‘No conversation too tough’, can help cancer, palliative and wider healthcare professionals support patients to communicate with their dependent children when a parent is dying. We examined perceptions of learning provided by the training, its contribution to confidence in communicating with families when a parent is dying, and subjective experience of, and reactions to, the training. We also explored potential changes in practice behaviours.
Pre–post, convergent, parallel, mixed-methods study. Motivations for practice change were measured quantitatively, and qualitatively through semi-structured interviews. Non-parametric analysis was conducted for self-efficacy and outcome expectancy measures; descriptive statistics examined perceptions of usefulness; intentions to use learning in practice and reactions to the training. Semi-structured interviews examined motivations and perceptions of learning in depth. A 6-week, practice log recorded immediate practice effects and reflections.
1-day training delivered 3 times, total delegates 36: online December 2021, February 2022, face-to-face March 2022. Questionnaires delivered correspondingly in online or paper formats, semi-structured interviews online.
Pre–post: palliative care professionals (n=14/12), acute cancer clinical nurse specialists (n=16/11), other healthcare professionals (n=5/5).
Positive changes were observed in self-efficacy (17 of 19 dimensions p
The training programme has the potential to effect change in practice behaviours. A large-scale study will evaluate the roll-out of the training delivered to individual professionals and whole teams across the UK. It will provide longer-term feedback to understand practice behaviour and mediators of change across professional roles.
by John Efromson, Giuliano Ferrero, Aurélien Bègue, Thomas Jedidiah Jenks Doman, Clay Dugo, Andi Barker, Veton Saliu, Paul Reamey, Kanghyun Kim, Mark Harfouche, Jeffrey A. Yoder
Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.
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